{"data":[{"id":1153,"title":"Introduction to Applied Statistics for Psychology Students","edition_statement":null,"volume":null,"copyright_year":2022,"ISBN10":null,"ISBN13":null,"license":"Attribution-NonCommercial-ShareAlike","language":"eng","accessibility_statement":null,"accessibility_features":"unknown","description":"Introduction to Applied Statistics for Psychology Students, by Gordon E. Sarty (Professor, Department of Psychology, University of Saskatchewan) began as a textbook published in PDF format, in various editions between 2014-2017. The book was written to meet the needs of University of Saskatchewan psychology students at the undergraduate (PSY 233, PSY 234) level. In 2019-2020, funding was provided through the Gwenna Moss Centre for Teaching and Learning, along with technical assistance from the Distance Education Unit, to update and adapt this book, making it more widely available in an easy-to-use and more adaptable digital (Pressbooks) format. The update also made revisions so that the book could be published with a license appropriate for open educational resources (OER). OERs are defined as “teaching, learning, and research resources that reside in the public domain or have been released under an intellectual property license that permits their free use and re-purposing by others” (Hewlett Foundation). This textbook and other OERs like it are openly licensed using a Creative Commons license, and are offered in various digital and e-book formats free of charge. Printed editions of this book can be obtained for a nominal fee through the University of Saskatchewan bookstore.","contributors":[{"id":5688,"contribution":"Author","primary":true,"corporate":false,"title":null,"first_name":"Gordon","middle_name":"E.","last_name":"Sarty","location":"University of Saskatchewan","background_text":"Gordon E. Sarty, University of Saskatchewan"}],"subjects":[{"id":35,"name":"Applied","parent_subject_id":7,"call_number":"QA37.3","visible_textbooks_count":48,"url":"https://open.umn.edu/opentextbooks/subjects/applied?locale=es"},{"id":7,"name":"Mathematics","parent_subject_id":null,"call_number":"QA1","visible_textbooks_count":177,"url":"https://open.umn.edu/opentextbooks/subjects/mathematics?locale=es"},{"id":82,"name":"Statistics","parent_subject_id":7,"call_number":"QA273-280","visible_textbooks_count":30,"url":"https://open.umn.edu/opentextbooks/subjects/statistics?locale=es"}],"publishers":[{"id":1132,"url":"https://openpress.usask.ca/","year":2022,"created_at":"2022-04-07T22:42:29.000-05:00","updated_at":"2022-04-07T22:42:29.000-05:00","name":"University of Saskatchewan"}],"formats":[{"id":2870,"type":"eBook","url":"https://openpress.usask.ca/introtoappliedstatsforpsych/","price":{"cents":0,"currency_iso":"USD"},"isbn":null},{"id":2871,"type":"PDF","url":"https://openpress.usask.ca/introtoappliedstatsforpsych/","price":{"cents":0,"currency_iso":"USD"},"isbn":null},{"id":2872,"type":"Online","url":"https://openpress.usask.ca/introtoappliedstatsforpsych/","price":{"cents":0,"currency_iso":"USD"},"isbn":null},{"id":2873,"type":"XML","url":"https://openpress.usask.ca/introtoappliedstatsforpsych/","price":{"cents":0,"currency_iso":"USD"},"isbn":null}],"rating":"4","textbook_reviews_count":2,"reviews":[{"id":33863,"first_name":"Mike","last_name":"Love","position":"Instructor","institution_name":"Lewis-Clark State College","comprehensiveness_rating":4,"comprehensiveness_review":"This text provides surface-level overview of many concepts in statistics and probability.","accuracy_rating":5,"accuracy_review":"I did not notice any significant errors in the presentation of formulas or definitions. Any variance I noticed could be easily dismissed as a difference in preferred notation or terminology.","relevance_rating":4,"relevance_review":"There is nothing wrong with the examples used, however, I would like t see more examples beyond abstract lists of numbers. Providing more word/application problems would be useful, particularly for independent learners.\r\n\r\nCourse mentions its intended use with specifically numbered psychology courses at author's institution (i.e. PSY 233/234). Would prefer a more generalized description of courses this is targeted to.","clarity_rating":4,"clarity_review":"The online version of the text has some formatting issues that make figures unclear. Some times, multi-line examples have \"broken\" alignment that makes their illustrative intent unclear.\r\n\r\nFor example, in 3.1.2: Median, if your viewing window is too small, the arrows pointing to relevant values can be horribly misaligned.\r\n\r\nIn 1.1.2 Intro to Univariate Statistics, the graphics used do not scale well with different zoom levels.","consistency_rating":4,"consistency_review":"The author seems to try and inject some humor into establishing some computational ideas, but that is difficult to express in a format like this. I sometimes felt the attempts at humor came across as judgmental or dismissive.","modularity_rating":5,"modularity_review":"The author has recommended modules based on course intent, but each section seems largely independent of the others, and can be used in different orders, based on need and curriculum preference.","organization_rating":5,"organization_review":"The organization is not the way I would have structured the text, but it is organized in a way that makes intuitive sense. The modularity of the text allows nicely for content to be re-arranged and re-organized.","interface_rating":5,"interface_review":"Links back to definitions and formulas make it easy to look back and check what symbols mean. Navigating through and across sections is very easy.","grammatical_rating":5,"grammatical_review":"No grammatical mistakes or vague wording that I noticed.","cultural_rating":5,"cultural_review":"I did not notice any potentially insensitive or offensive content or presentation.","overall_rating":9,"overall_review":"I do not feel the material goes quite deep enough for STEM students or for curious Psychology students, but also manages to not quite focus enough on meaning and application for students to build an intuitive understanding of what the measurements mean and how they can be used.\r\n\r\n A good start for a statistics class, but faculty would need to supplement with more detailed examples and exercises.\r\n\r\nNot particularly useful for an independent learner outside of a motivated STEM student","created_at":"2022-05-18T15:16:16.000-05:00","updated_at":"2022-05-18T15:16:16.000-05:00"},{"id":34894,"first_name":"Pam","last_name":"Ansburg","position":"Professor","institution_name":"Metropolitan State University of Denver","comprehensiveness_rating":3,"comprehensiveness_review":"The book covers the main ideas that one would want to cover in the a beginning stats course for psychology majors, but the degree to which the topics are covered is quite shallow. Not that I am looking for more math or proofs, just more context and a slower more methodical lead in to the material. Students may get spooked without that gentle introduction.","accuracy_rating":5,"accuracy_review":"As far as I can tell all looks good here in terms of accuracy.","relevance_rating":3,"relevance_review":"The reliance on SPSS throughout may make the book fall out of fashion. Many are moving to free software packages such as Jamovi. The authors do justify their inclusion of SPSS in their introduction so I am not levelling a criticism here. Just a note that embedding examples that rely on using the wildly costly SPSS package for data analysis is likely limit the number of instructors who will be able to adopt the text.","clarity_rating":2,"clarity_review":"This is where I really feel the textbook falls short. I had high hopes for the book because it has \"applied\" in the title. Instead of situating the introduction of statistical methods in rich applied contexts, the book really presents the statistical analyses in a bare bones manner. What is presented is clear, but it is not presented in an inviting tone and accessible context.","consistency_rating":5,"consistency_review":"This book does clearly and consistently use statistical symbols throughout.","modularity_rating":4,"modularity_review":"Of course, statistics does require a cumulative understanding of some early concepts that can be applied to more advanced concepts; but beyond that characteristic of the topic, it is easy to see how an instructor could select to cover certain sections and/or rearrange the presentation of content.","organization_rating":5,"organization_review":"The order in which topics are presented aligns with a traditional textbook structure.. The order of topics makes sense. The one section I might consider reordering is to move the discussion of percentiles and quartiles to the section on frequency. But, I can see why the author placed that section where they did.","interface_rating":4,"interface_review":"The book seemed to work well overall. However, there is some commentary located as footnotes on some pages. These comments often humanize the author and are inviting /comforting notes to students. By placing them as footnotes, they seem to be just stuck at the bottom of the page as if they are not to be integrated with the main text.  I think those comments are terrific and would like to see more of them and to seem them highlighted throughout.","grammatical_rating":5,"grammatical_review":"The book is well, but sparsely, written. Again, for a book with the word \"applied\" in the title, I would have liked to have heard more of the authors' voice, like we do in the aforementioned commentaries.","cultural_rating":5,"cultural_review":"I did not notice any offensive or insensitive examples. However, some may find the reliance on using a binary gender construct as the basis for tests that compare two groups (e.g., Unpaired Z- tests) as inconsistent with inclusive teaching.","overall_rating":8,"overall_review":"Kudos to the author for writing a clear and concise text; but, in a class like statistics which often engenders fear in the heart of psychology majors, there needs to be many more applied examples and a warmer handoff to the statistical formulae.","created_at":"2024-02-15T17:24:27.000-06:00","updated_at":"2024-02-15T17:24:27.000-06:00"}],"url":"https://open.umn.edu/opentextbooks/textbooks/introduction-to-applied-statistics-for-psychology-students?locale=es","updated_at":"2026-05-11T02:08:24.000-05:00"},{"id":1083,"title":"Mostly Harmless Elementary Statistics","edition_statement":"1st Edition","volume":null,"copyright_year":2023,"ISBN10":null,"ISBN13":null,"license":"Attribution-ShareAlike","language":"eng","accessibility_statement":"","accessibility_features":"","description":"This text is for an introductory level probability and statistics course with an intermediate algebra prerequisite. The focus of the text follows the American Statistical Association’s Guidelines for Assessment and Instruction in Statistics Education (GAISE). Software examples provided for Microsoft Excel, TI-84 \u0026 TI-89 calculators. Students new to statistics are sure to benefit from this ADA accessible and relevant textbook. The examples are current and resonate with everyday life. The casual narrative style, has a conversational tone to provide an inclusive and easy to read format for students.","contributors":[{"id":5540,"contribution":"Author","primary":true,"corporate":false,"title":null,"first_name":"Rachel","middle_name":"L.","last_name":"Webb","location":"Portland State University","background_text":""}],"subjects":[{"id":35,"name":"Applied","parent_subject_id":7,"call_number":"QA37.3","visible_textbooks_count":48,"url":"https://open.umn.edu/opentextbooks/subjects/applied?locale=es"},{"id":7,"name":"Mathematics","parent_subject_id":null,"call_number":"QA1","visible_textbooks_count":177,"url":"https://open.umn.edu/opentextbooks/subjects/mathematics?locale=es"},{"id":82,"name":"Statistics","parent_subject_id":7,"call_number":"QA273-280","visible_textbooks_count":30,"url":"https://open.umn.edu/opentextbooks/subjects/statistics?locale=es"}],"publishers":[{"id":1061,"url":"https://pdxscholar.library.pdx.edu/","year":null,"created_at":"2021-10-13T10:34:31.000-05:00","updated_at":"2021-10-13T10:34:31.000-05:00","name":"Portland State University Library"}],"formats":[{"id":2656,"type":"PDF","url":"https://mostlyharmlessstat.wixsite.com/webpage/mostly-harmless-elementary-statistics","price":{"cents":0,"currency_iso":"USD"},"isbn":null},{"id":2655,"type":"Hardcopy","url":"https://www.lulu.com/en/us/shop/rachel-l-webb-and-james-tadlock/mostly-harmless-statistics/paperback/product-pwemgj.html?page=1\u0026pageSize=4","price":{"cents":0,"currency_iso":"USD"},"isbn":null}],"rating":"3","textbook_reviews_count":1,"reviews":[{"id":34435,"first_name":"Rachelle","last_name":"Bryant","position":"Adjunct Math and Criminal Justice Faculty","institution_name":"City Colleges of Chicago","comprehensiveness_rating":3,"comprehensiveness_review":"This OER Textbook includes most concepts that would (and should) be covering in an Introductory (or Elementary) Statistics course.  There are; however, a few concepts that I feel were \"left out\" (whether on purpose or inadvertently) that almost every Introductory (or Elementary) Statistics course covers (at least the ones that I have been required to teach at community colleges or universities anyway).  For on example, the Types of Measurement Scales are usually an entire section in almost every Statistics textbook; yet, in this OER textbook, the author spends less than one page covering this topic and there are very few examples of the different types. Another concept that is very lacking is the concept of the difference between Statistical and Practical Significance.  The author only discusses that once, and it is not until Page 226 that this topic is discussed.  Even then, a staunch definition is not even provided for either type of significance; the definition that is provided is very vague and short.  There is also no definition provided for a very important type of sampling, which is that of voluntary response sampling. Another example is the lack of importance put on the definition of bias by this author. While the definition of bias is provided, the word \"bias\" is not even in bold, as though it is not important.  Non-response bias is not even discussed by this author. The author also only mentions the \"pitfalls\"  or reasons why data can be misleading in Section 1.3, instead of going into depth about each different kind of bias and examples of each different kind of bias.  In Statistics, bias is an extremely important concept, and the author acts like it is unimportant to the study of statistics.  Confounding variables, as well as lurking variables, are not discussed until Page 375, in the regression chapter, when they are supposed to be (and are) discussed in other textbooks) in the first chapter in the same section as Observational Studies and Designed Experiments.","accuracy_rating":3,"accuracy_review":"Personally, I think the author was very biased in what they chose to incorporate into this OER textbook and how they chose to incorporate it.  I would not consider much of the content to be \"accurate.\" One of the definitions of \"accurate\" in Miriam Webster's online dictionary, www.m-w.com, is: going to, reaching, or hitting the intended target : not missing the target.\" In this aspect, I do not think this OER textbook is accurate in many aspects.  As previously mentioned, there are significant \"omissions\" of concepts that should have been discussed, but are not in this Statistics textbook, and there is a serious problem of lack of breadth and depth in this OER textbook regarding many topics.  Examples include bias, types of bias and their examples, confounding and lurking variables, statistical versus practical significance, and correlation and regression.  While I did not find any errors in calculations, I do not consider a textbook with so many important \"omissions\" and/or lack of breadth and depth of so many important statistical concepts to be \"error-free\" and \"unbiased.\"  This is why I give this OER textbook only a 3 for accuracy.","relevance_rating":3,"relevance_review":"I will agree that the content is up-to-date and will not make the text obsolete within a short period of time.  I do like the fact that the author does bring up and show several pictures of calculator concepts for the TI-83, 84, and even 89.  I think that is extremely helpful for students.  However, I do think that when discussing correlation, the author should have brought up the easy way to find the correlation in the calculator, which would simply be to enter the values for the x variable in List 1 (L1), enter the values for the y-variable in List 2 (L2) and then instead of calculating 2 variable statistics (2 Var Stats) as the author suggests, the author should have instead told students to do the following.  Press the 2nd button and then the 0 button (which stands for Catalog). Students should then arrow all the way down to \"Diagnostic On\" and press Enter twice.  The calculator will say \"Done.\"  Then, the students can then press the STATS button, arrow over to CALC, and then go down to #4, which is LinReg(ax + b) or #8, which is LinReg(a + bx), and then they can make sure that List 1 shows up as L1 and List 2 shows up as L2 and then they can arrow down to Calculate and it will show the values of r and r squared and will also give the values you need to come up with your linear regression equation.  The author did not provide this information to students and I think this is a major disadvantage, because instead the author simply shows students how to obtain the formula values from the calculator and then tells students to plug those values into the formula.  The whole idea of using the calculator is to be able to avoid the use of formulas and make it easier on yourself.  So while I think the author's showings of calculator concepts are very beneficial to students, I think there certainly is room for improvement.  However, the author would need to re-organize her entire Chapter 12 on regression and put in into two totally different chapters - one before probability with the non-inference concepts and then leave only the inference concepts regarding regression in Chapter 12.  I also think that if the author really wanted ALL students to benefit from using this OER material, that they should have incorporated how to use Excel to solve these problems as well, not just how to solve them with calculators.  Those updates would be easy to include; however, they would take a long time to update.","clarity_rating":3,"clarity_review":"I think the clarity of this OER text is just \"average.\"  According to Miriam Webster's online dictionary, www.m-w.com, one of the definitions of lucid is, \"clear to the understanding.\"  I think that the author means well; however, several of her definitions are very unclear because they do not go into enough breadth and depth for those definitions.  Examples of this include her definitions of bias and her lack of definitions of statistical versus practical significance, which they only discusses once and does not really provide a definition for at all.  Bias they do provide a definition for, but it is extremely short and there is not enough depth, as they do not discuss the different types of bias and does not give examples of those different types of bias in any reasonable detail.","consistency_rating":2,"consistency_review":"According to https://pressbooks.bccampus.ca/openubcpub/chapter/textbook-design-rules-open-ubc/, \"A textbook is an organized body of material useful for the formal study of a subject area. It should be discrete, and well-bounded in scope and the text material should relate to a solid understanding of the subject, usually mixing theory and practice for each topic as it covers the subject domain. The textbook should also use examples and problems to assist the student in better grasping each presented concept by following examples, and then applying the concept in structured exercises or problems. The textbook should have an internally consistent style and there should be little or no surprises for the student in terms of layout and presentation of material. The texts user can get comfortable with the layout, the tempo of presentation, and the pattern of figures, illustrations, examples, and exercises. Once reviewed, the textbook should isolate material that is useful to the future application of subject knowledge in well-organized appendices and tables. Finally, the textbook is a structured resource and is not just a collection of useful material. The textbook is a guide for the student for an order of review that will aid in mastering the subject area. Topics are presented in major parts, chapters, sections, and subsections that are organized in a way that facilitates understanding. This means that the text’s organization is based on the intersection of two requirements. The first of these are the requirements of the subject domain. Since most textbooks are developed by, or based on the contributions of subject matter experts, this requirement is usually well attended to.\" I honestly do not find this OER material to be \"well-bounded in scope\" and I certainly do not feel that the, \"text material...relate(s) to a solid understanding of the subject.\" To the contrary, I find that there are many topics that are \"left out\" of this OER textbook that I find to be extremely important and, even ones that are in this textbook, many of them certainly do NOT give students, \"a solid understanding of the subject.\" Examples include topics such as bias, statistical versus practical significance, confounding and lurking variables, and the levels of measurement.  As far as, \"The textbook should also use examples and problems to assist the student in better grasping each presented concept by following examples, and then applying the concept in structured exercises or problems,\" this OER textbook does not give examples and problems during several of the sections of the textbook, nor does it give any examples and problems after each section of the textbook for students to practice their skills.  To the contrary, instead, the author puts all problems for students to complete AFTER the entire chapter and gives students no chances to practice throughout the Chapter, such as Section Review exercises or Check Your Understanding Problems. Chapter 1 does not even have examples of many things in the Sections of the Chapter.  I will say that the section on Misleading Graphs does have examples and they are good ones - the author should consider actually doing things like this for EVERY single section in the OER textbook, not just having one section have comprehensive sets of examples.  The author also needs to have Section Review Exercises after each section and should certainly needs to have more examples throughout every section of the topics (more breadth and depth for significant understanding of the topics) and also needs to have more examples where students can practice their skills throughout sections.","modularity_rating":3,"modularity_review":"I find the modularity of this text to be reasonable.  The author does divide the text into smaller sections; however, some of them are way too small to amount to anything, and, some of them, though smaller than most Statistics textbooks, are too long because they still have too many concepts in one small section that really should be broken up into two or more sections and should have more breadth and depth to them.  A perfect example is Chapter 1.  Section 1-1 basically talks about almost nothing (although, to the author's credit, most Statistics textbooks only use the first section of a textbook as an introduction).  However, section 1-2 is way too small and section 1-3 should have actually been changed to be at least two (if not three) sections individually by themselves. The author does do a good job of subheadings; however, when it comes to definitions, some of the most important ones are not in bold and are not emphasized when they really need to be. As a Statistics teacher, I would have to literally re-arrange many of the sections of this book and would have to supplement many of the sections with other material that should be covered in an Introductory Statistics course is either not in this book, or in the wrong place, or not emphasized in the way it should be.  For that reason, I only give the modality a two.","organization_rating":2,"organization_review":"Unfortunately, the way the author has organized this OER text, it is not organized at all nearly as well as regular Statistics textbooks.  It leaves a ton of concepts out in the beginning that should be taught in Chapter 1, and does not spend nearly enough time on several topics that are clearly important to Statistics. Things like not discussing lurking and confounding variables in Chapter 1 and waiting until Page 375 to discuss Regression and that is the first time that students see the terms confounding and lurking variables is one example of why this text's organization is so poor.  The logic and order of how the concepts are presented is poor as well.  Waiting until Chapter 12 to discuss Correlation and Regression is very poor organization and a complete disadvantage to students enrolled in Statistics.  Correlation and Regression (other than inference tests for Regression) should be taught before Probability sections.  Although it is true that some Statistics textbooks do the same thing the author did and teach Correlation and Regression after Inferences of One and Two Samples involving proportions and means, but before Chi-Squared tests, that does not mean that this it the best thing for students, either. In addition to thinking that the content in Chapter 12 is completely out of place, the order in which it is presented is also very confusing for students.  The hypothesis tests are the only things that should be covered in Chapter 12; again, the other topics should have been covered in a separate chapter before probability is covered.  In addition to this, Multiple Linear Regression is NEVER taught in an Introductory (or Elementary) Statistics course, as it is never on the SLO's.  Therefore, it should not be in this OER textbook, or, if the author insists on including it, it should be in a different chapter of \"Additional Topics of Interest\" that students can take a look at if they are interested; however, the students this OER textbook was designed to serve would never be studying multiple linear regression in an Introductory Statistics course, so it is pointless to have that topic in this OER textbook, other than at the end of the textbook as some type of \"Additional Topic(s) of Interest.\"","interface_rating":4,"interface_review":"I think the interface of the OER textbook is just fine.  I did not find any navigation problems or distortions of images or charts.  However, I do think there are other things that could confuse or distract the reader, such as the serious level of \"omissions\" and the lack of breadth and depth of certain concepts discussed in the OER textbook.  This is why I gave it only a 4 instead of a 5.","grammatical_rating":4,"grammatical_review":"While I did not find any grammatical errors, I am not an English teacher; I am a Math and Statistics teacher.  What I did find were many \"omissions\", even if they were inadvertent (though it appeared that the author did pick and choose what they wanted to go into this OER textbook, so the \"omissions\" appeared to be purposeful). Therefore, I am giving this section a four due to the level of \"omissions\" that I found.","cultural_rating":3,"cultural_review":"I think that the level of \"omissions\" and the lack of breadth and depth to several of the subject areas of Statistics that the author discussions (or omits and never discusses) are actually a lack of respect to anyone attempting to learn Statistics in the first place.  If you are going to teach students Statistics, you should teach it in a way, shape, and form that students can understand.  The way the author presents this material leaves so many questions unanswered and there are so many \"omissions\" and things that should have been discussed that were not.  Students should be taught Statistics in detail because it is the most valuable real world Mathematics that any student can and will ever learn in their lifetime.  I also think that if the author really wanted to cut costs for students, they should have incorporated how to use Excel to solve statistical problems, since Excel is free for all students with their paid tuition; however, a calculator is not free (even if at the author's institution calculators are provided for each student, if they even are, that is almost never the case at regular institutions). The way the author presents the topics and/or omits important topics and/or does not provide anywhere near enough detail into certain topics makes me give this OER textbook a \"3\" rating.","overall_rating":6,"overall_review":"When I first saw that there was an OER textbook designed for Statistics, I was beyond thrilled.  I laughed at the title and thought this would be a great resource and that I might be able to use it while teaching and that I could go to my department chairs and recommend this resource to be used in our classes, so that we could literally bring down the cost of the Statistics course materials for students.  Unfortunately, after looking into this book in more detail, it is one that I would only recommend as a resource for students to \"look up\" certain concepts. I could never use this OER textbook as a primary textbook in my Statistics courses and I could never recommend that it be the primary resource for students to have access to and use of because there are way too many important \"omissions\" and the concepts that are covered are not covered in anywhere near enough breadth and depth.  The Statistics student needs to understand WHY they are learning the concepts.  With what the author has presented, they leave that question unanswered with their omissions.  I also do not like the fact at all that many of the sections do not have any examples for students of certain concepts and definitely do not like the fact that there are no examples for students to practice and complete until after the entire Chapter and not after each section.  I also found that the structure of the textbook is just not student friendly, since most of the concepts in Chapter 12 should be taught before Probability concepts and they are not.  Leaving those concepts until Chapter 12 means that several instructors will not even get to them, and Correlation and Regression are absolute necessities for students to learn in Introductory Statistics courses.  I also, as I mentioned earlier, think that the author should consider incorporating how to use Excel to solve certain conceptual problems and not just the calculator.  Not every student can afford a calculator and online calculators are no longer free (they were the first two years of COVID; however, they are no longer free).  However, every student receives a Microsoft Office subscription, including Excel, to Office 365, as part of their paid tuition every semester.  Therefore, the calculator is an added expense for students; whereas Excel is not an added expense.  So if the author is really trying to cut costs for all students, they should also add how to use Excel to solve statistical concepts to this book.","created_at":"2023-03-06T04:09:44.000-06:00","updated_at":"2023-03-06T04:09:44.000-06:00"}],"url":"https://open.umn.edu/opentextbooks/textbooks/mostly-harmless-statistics?locale=es","updated_at":"2026-05-11T02:05:44.000-05:00"},{"id":1044,"title":"Introduction to Statistical Thinking","edition_statement":null,"volume":null,"copyright_year":2010,"ISBN10":null,"ISBN13":null,"license":"Attribution","language":"eng","accessibility_statement":null,"accessibility_features":"unknown","description":"The target audience for this book is college students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more. It is assumed that the students do have basic skills in using computers and have access to one. Moreover, it is assumed that the students are willing to actively follow the discussion in the text, to practice, and more importantly, to think. Teaching statistics is a challenge. Teaching it to students who are required to learn the subject as part of their curriculum, is an art mastered by few. In the past I have tried to master this art and failed. In desperation, I wrote this book. This book uses the basic structure of generic introduction to statistics course. However, in some ways I have chosen to diverge from the traditional approach. One divergence is the introduction of R as part of the learning process. Many have used statistical packages or spreadsheets as tools for teaching statistics. Others have used R in advanced courses. I am not aware of attempts to use R in introductory level courses. Indeed, mastering R requires much investment of time and energy that may be distracting and counterproductive for learning more fundamental issues. Yet, I believe that if one restricts the application of R to a limited number of commands, the benefits that R provides outweigh the difficulties that R engenders. Another departure from the standard approach is the treatment of probability as part of the course. In this book I do not attempt to teach probability as a subject matter, but only specific elements of it which I feel are essential for understanding statistics. Hence, Kolmogorov’s Axioms are out as well as attempts to prove basic theorems and a Balls and Urns type of discussion. On the other hand, emphasis is given to the notion of a random variable and, in that context, the sample space.","contributors":[{"id":5485,"contribution":"Author","primary":true,"corporate":false,"title":null,"first_name":"Benjamin","middle_name":null,"last_name":"Yakir","location":"The Hebrew University of Jerusalem","background_text":"Benjamin Yakir, The Hebrew University of Jerusalem"}],"subjects":[{"id":3,"name":"Computer Science","parent_subject_id":null,"call_number":"QA76","visible_textbooks_count":137,"url":"https://open.umn.edu/opentextbooks/subjects/computer-science-information-systems?locale=es"},{"id":35,"name":"Applied","parent_subject_id":7,"call_number":"QA37.3","visible_textbooks_count":48,"url":"https://open.umn.edu/opentextbooks/subjects/applied?locale=es"},{"id":7,"name":"Mathematics","parent_subject_id":null,"call_number":"QA1","visible_textbooks_count":177,"url":"https://open.umn.edu/opentextbooks/subjects/mathematics?locale=es"},{"id":82,"name":"Statistics","parent_subject_id":7,"call_number":"QA273-280","visible_textbooks_count":30,"url":"https://open.umn.edu/opentextbooks/subjects/statistics?locale=es"}],"publishers":[{"id":1022,"url":"https://pluto.mscc.huji.ac.il/~msby/","year":null,"created_at":"2021-08-27T02:04:09.000-05:00","updated_at":"2021-08-27T02:04:27.000-05:00","name":"Benjamin Yakir"}],"formats":[{"id":2565,"type":"PDF","url":"https://eleuven.github.io/statthink/statthink.pdf","price":{"cents":0,"currency_iso":"USD"},"isbn":null},{"id":2566,"type":"Online","url":"https://eleuven.github.io/statthink/","price":{"cents":0,"currency_iso":"USD"},"isbn":null}],"rating":"4","textbook_reviews_count":1,"reviews":[{"id":33534,"first_name":"Xuan","last_name":"Wang","position":"Assistant Professor","institution_name":"University of Texas Rio Grande Valley","comprehensiveness_rating":4,"comprehensiveness_review":"This book covers most of the points that should be introduced in the entry-level statistical course. The author has brought out descriptive analytics, I think it will be better if predictive and prescriptive analytics can be introduced in the book. Also, it can be a bonus if big data is talked about in this book.","accuracy_rating":4,"accuracy_review":"Some of the contents need to add more information and explanations.","relevance_rating":3,"relevance_review":"Some of the additional contents can be added in each chapter, such as in the chapter of sampling and data structures, there should be mentioned the four different data, interval, ratio, nominal, and ordinal, which help the readers to understand how to take care different data structures, Therefore, some contents need to be considered.","clarity_rating":4,"clarity_review":"The book is mainly based on the application of R programming, I think it will be better to give a better explanation before it starts to introduce the usage of applying R programming.","consistency_rating":5,"consistency_review":"This book holds the consistency.","modularity_rating":4,"modularity_review":"There are many examples that are applying the R programming, the logic is very important, It will be better if an additional vocabulary of the R language in each chapter, and the reader will be easier to know the coding that is referring to the different context that is associated with R programming.","organization_rating":3,"organization_review":"The organization of the book is a little bit confused, I think the order of sections should be arranged in this book. For example, the author places descriptive statistics as the third chapter, I think it can be put in the later chapter because the different data type has not been introduced before the reader knows how to do the descriptive statistics with the different type of variable.","interface_rating":4,"interface_review":"The book has provided the navigation for each chapter, figure, and table. I think it will be better if the terminologies can be provided at the end of the textbook, and it will be additional navigation for the reader who wants to direct to the pages with the relevant contents.","grammatical_rating":4,"grammatical_review":"There are some grammar issues that need to be fixed in the book.","cultural_rating":5,"cultural_review":"IT is not culturally insensitive or offensive at all.","overall_rating":8,"overall_review":"I think the most important thing that I would like to recommend is to add some content and reorganize the chapters.","created_at":"2021-12-13T22:18:48.000-06:00","updated_at":"2021-12-13T22:18:48.000-06:00"}],"url":"https://open.umn.edu/opentextbooks/textbooks/introduction-to-statistical-thinking?locale=es","updated_at":"2026-05-11T02:05:39.000-05:00"},{"id":459,"title":"Introduction to Statistics","edition_statement":null,"volume":null,"copyright_year":2003,"ISBN10":null,"ISBN13":null,"license":"No Rights Reserved","language":"eng","accessibility_statement":"","accessibility_features":"","description":"Introduction to Statistics is a resource for learning and teaching introductory statistics. This work is in the public domain. Therefore, it can be copied and reproduced without limitation. However, we would appreciate a citation where possible. Please cite as: Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University. Instructor's manual, PowerPoint Slides, and additional questions are available.","contributors":[{"id":4223,"contribution":"Author","primary":true,"corporate":false,"title":null,"first_name":"David","middle_name":null,"last_name":"Lane","location":"Rice University","background_text":"David Lane is an Associate Professor in the Departments of Psychology, Statistics, and Management at the Rice University. Lane is the principal developer of this resource although many others have made substantial contributions. This site was developed at Rice University, University of Houston-Clear Lake, and Tufts University."}],"subjects":[{"id":35,"name":"Applied","parent_subject_id":7,"call_number":"QA37.3","visible_textbooks_count":48,"url":"https://open.umn.edu/opentextbooks/subjects/applied?locale=es"},{"id":7,"name":"Mathematics","parent_subject_id":null,"call_number":"QA1","visible_textbooks_count":177,"url":"https://open.umn.edu/opentextbooks/subjects/mathematics?locale=es"},{"id":82,"name":"Statistics","parent_subject_id":7,"call_number":"QA273-280","visible_textbooks_count":30,"url":"https://open.umn.edu/opentextbooks/subjects/statistics?locale=es"}],"publishers":[{"id":378,"url":"http://onlinestatbook.com/2/index.html","year":null,"created_at":"2018-09-07T12:22:39.000-05:00","updated_at":"2020-01-02T22:41:19.000-06:00","name":"David Lane"}],"formats":[{"id":651,"type":"PDF","url":"http://onlinestatbook.com/Online_Statistics_Education.pdf","price":{"cents":0,"currency_iso":"USD"},"isbn":null},{"id":652,"type":"Online","url":"http://onlinestatbook.com/2/index.html","price":{"cents":0,"currency_iso":"USD"},"isbn":null},{"id":653,"type":"eBook","url":"http://onlinestatbook.com/Online_Statistics_Education.epub","price":{"cents":0,"currency_iso":"USD"},"isbn":null}],"rating":"4.5","textbook_reviews_count":15,"reviews":[{"id":1492,"first_name":"David","last_name":"jabon","position":"Associate Professor","institution_name":"DePaul University","comprehensiveness_rating":5,"comprehensiveness_review":"This text covers all the standard topics in a semester long introductory course in statistics.  It is particularly well indexed and very easy to navigate.  There is comprehensive hyperlinked glossary.","accuracy_rating":5,"accuracy_review":"The material is completely accurate.  There are no errors. The terminology is standard with one exception: the book calls what most people call the interquartile range, the H-spread in a number of places.  Ideally, the term \"interquartile range\" would be used in place of every reference to \"H-spread.\"  \"Interquartile range\" is simply a better, more descriptive term of the concept that it describes.  It is also more commonly used nowadays.","relevance_rating":5,"relevance_review":"This book came out a number of years ago, but the material is still up to date.  Some more recent case studies have been added.","clarity_rating":5,"clarity_review":"The writing is very clear. There are also videos for almost every section.  The section on boxplots uses a lot of technical terms that I don't find are very helpful for my students (hinge, H-spread, upper adjacent value).","consistency_rating":5,"consistency_review":"The text is internally consistent with one exception that I noted (the use of the synonymous words \"H-spread\" and \"interquartile range\").","modularity_rating":5,"modularity_review":"The text book is brokenly into very short sections, almost to a fault.  Each section is at most two pages long.  However at the end of each of these sections there are a few multiple choice questions to test yourself.  These questions are a very appealing feature of the text.","organization_rating":4,"organization_review":"The organization, in particular the ordering of the topics, is rather standard with a few exceptions.  Boxplots are introduced in Chapter II before the discussion of measures of center and dispersion.  Most books introduce them as part of discussion of summaries of data using measure of center and dispersion.   Some statistics instructors may not like the way the text lumps all of the sampling distributions in a single chapter (sampling distribution of mean, sampling distribution for the difference of means, sampling distribution of a proportion, sampling distribution of r).  I have tried this approach, and I now like this approach. But it is a very challenging chapter  for students.","interface_rating":5,"interface_review":"The book's interface has no features that distracted me.  Overall the text is very clean and spare, with no additional distracting visual elements.","grammatical_rating":5,"grammatical_review":"The book contains no grammatical errors.","cultural_rating":5,"cultural_review":"The book's cultural relevance comes out in the case studies.  As of this writing there are 33 such case studies, and they cover a wide range of issues from health to racial, ethnic, and gender disparity.","overall_rating":10,"overall_review":"Each chapter as a nice set of exercises with selected answers.  The thirty three case studies are excellent and can be supplement with some other online case studies.  An instructor's manual and PowerPoint slides can be obtained by emailing the author.   There are direct links to online simulations within the text.  This text is very high quality textbook in every way.","created_at":"2017-08-15T19:00:00.000-05:00","updated_at":"2017-08-15T19:00:00.000-05:00"},{"id":1745,"first_name":"Zaki","last_name":"Kuruppalil","position":"Associate Professor","institution_name":"Ohio University","comprehensiveness_rating":5,"comprehensiveness_review":"This is a comprehensive book on statistical methods, its settings and most importantly the interpretation of the results. With the advent of computers and software’s, complex statistical analysis can be done very easily. But the challenge is the knowledge of how to set the case, setting parameters (for example confidence intervals) and knowing its implication on the interpretation of the results. If not done properly this could lead to deceptive inferences, inadvertently or purposely. This book does a great job in explaining the above using many examples and real world case studies. If you are looking for a book to learn and apply statistical methods, this is a great one. I think the author could consider revising the title of the book to reflect the above, as it is more than just an introduction to statistics, may be include the word such as practical guide.  ","accuracy_rating":5,"accuracy_review":"The contents of the book seems accurate. Some plots and calculations were randomly selected and checked for accuracy.\n","relevance_rating":5,"relevance_review":"The book topics are up to date and in my opinion, will not be obsolete in the near future. I think the smartest thing the author has done is, not tied the book with any particular software such as minitab or spss . No matter what the software is, standard deviation is calculated the same way as it is always. The only  noticeable exception in this case was using the Java Applet for calculating Z values in page 261 and in page 416 an excerpt of SPSS analysis is provided for ANOVA calculations.","clarity_rating":5,"clarity_review":"The contents and examples cited are clear and explained in simple language. Data analysis and presentation of the results  including mathematical calculations, graphical explanation using charts, tables, figures etc are presented with clarity.","consistency_rating":5,"consistency_review":"Terminology is consistant. Framework for each chapter seems consistent with each chapter beginning with a set of defined topics, and each of the topic divided into modules with each module having a set of learning objectives and prerequisite chapters. ","modularity_rating":5,"modularity_review":"The text book is divided into chapters with each chapter further divided into modules. Each of the modules have detailed learning objectives and prerequisite required. So you can extract a portion of the book and use it as a standalone to teach certain topics or as a learning guide to apply a relevant topic.","organization_rating":4,"organization_review":"Presentation of the topics are well thought and are presented  in a logical fashion as if it would be introduced to someone who is learning the contents. However, there are some issues with table of contents and page numbers, for example chapter 17 starts in page 597 not 598. Also some tables and figures does not have a number, for instance the graph shown in page 114 does not have a number. Also it would have been better if the chapter number was included in table and figure identification, for example Figure 4-5 . Also in some cases, for instance page 109, the figures and titles are in two different pages.","interface_rating":4,"interface_review":"No major issues. Only suggestion would be, since each chapter has several modules, any means such as a header to trace back where you are currently, would certainly help.","grammatical_rating":4,"grammatical_review":"Easy to read and phrased correctly in most cases. Minor grammatical errors such as missing prepositions etc. In some cases the author seems to have the habbit of using a period after the decimal. For instance page 464, 467 etc. \nFor X = 1,\nY' = (0.425)(1) + 0.785 = 1.21.\nFor X = 2,\nY' = (0.425)(2) + 0.785 = 1.64.","cultural_rating":4,"cultural_review":"However it contains some statements (even though given as examples) that could be perceived as subjective, which the author could consider citing the sources. For example from page 11: \nStatistics include numerical facts and figures. For instance:\n• The largest earthquake measured 9.2 on the Richter scale.\n• Men are at least 10 times more likely than women to commit murder.\n• One in every 8 South Africans is HIV positive.\n• By the year 2020, there will be 15 people aged 65 and over for every new baby\nborn.","overall_rating":9,"overall_review":"Solutions for the exercises would be a great teaching resource to have","created_at":"2018-02-01T18:00:00.000-06:00","updated_at":"2018-02-01T18:00:00.000-06:00"},{"id":1796,"first_name":"Randy","last_name":"Vander Wal","position":"Professor","institution_name":"The Pennsylvania State University","comprehensiveness_rating":5,"comprehensiveness_review":"As a text for an introductory course, standard topics are covered.   It was nice to see some topics such as power, sampling, research design and distribution free methods covered, as these are often omitted in abbreviated texts. Each module introduces the topic, has appropriate graphics, illustration or worked example(s) as appropriate and concluding with many exercises. An instructor’s manual is available by contacting the author. A comprehensive glossary provides definitions for all the major terms and concepts. The case studies give examples of practical applications of statistical analyses. Many of the case studies contain the actual raw data. To note is that the on-line e-book provides several calculators for the essential distributions and tests. These are provided in lieu of printed tables which are not included in the pdf. (Such tables are readily available on the web.)","accuracy_rating":5,"accuracy_review":"The content is accurate and error free. Notation is standard and terminology is used accurately, as are the videos and verbal explanations therein. Online links work properly as do all the calculators. The text appears neutral and unbiased in subject and content.","relevance_rating":5,"relevance_review":"The text achieves contemporary relevance by ending each section with a Statistical Literacy example, drawn from contemporary headlines and issues. Of course, the core topics are time proven. There is no obvious material that may become “dated”.","clarity_rating":4,"clarity_review":"The text is very readable. While the pdf text may appear “sparse” by absence varied colored and inset boxes, pictures etc., the essential illustrations and descriptions are provided. Meanwhile for this same content the on-line version appears streamlined, uncluttered, enhancing the value of the active links. Moreover, the videos provide nice short segments of “active” instruction that are clear and concise. Despite being a mathematical text, the text is not overly burdened by formulas and numbers but rather has “readable feel”.","consistency_rating":5,"consistency_review":"This terminology and symbol use are consistent throughout the text and with common use in the field. The pdf text and online version are also consistent by content, but with the online e-book offering much greater functionality.","modularity_rating":5,"modularity_review":"The chapters and topics may be used in a selective manner. Certain chapters have no pre-requisite chapter and in all cases, those required are listed at the beginning of each module.  It would be straightforward to select portions of the text and reorganize as needed. The online version is highly modular offering students both ease of navigation and selection of topics. ","organization_rating":5,"organization_review":"Chapter topics are arranged appropriately. In an introductory statistics course, there is a logical flow given the buildup to the normal distribution, concept of sampling distributions, confidence intervals, hypothesis testing, regression and additional parametric and non-parametric tests. The normal distribution is central to an introductory course. Necessary precursor topics are covered in this text, while its use in significance and hypothesis testing follow, and thereafter more advanced topics, including multi-factor ANOVA.\n\nEach chapter is structured with several modules, each beginning with pre-requisite chapter(s), learning objectives and concluding with Statistical Literacy sections providing a self-check question addressing the core concept, along with answer, followed by an extensive problem set. The clear and concise learning objectives will be of benefit to students and the course instructor. No solutions or answer key is provided to students. An instructor’s manual is available by request.\n","interface_rating":5,"interface_review":"The on-line interface works well. In fact, I was pleasantly surprised by its options and functionality. The pdf appears somewhat sparse by comparison to publisher texts, lacking pictures, colored boxes, etc. But the on-line version has many active links providing definitions and graphic illustrations for key terms and topics. This can really facilitate learning as making such “refreshers” integral to the new material. Most sections also have short videos that are professionally done, with narration and smooth graphics. In this way, the text is interactive and flexible, offering varied tools for students. To note is that the interactive e-book works for both IOS and OS X. ","grammatical_rating":5,"grammatical_review":"The text in pdf form appeared to free of grammatical errors, as did the on-line version, text, graphics and videos.","cultural_rating":3,"cultural_review":"This text contains no culturally insensitive or offensive content. The focus of the text is on concepts and explanation. ","overall_rating":9,"overall_review":"The text would be a great resource for students. The full content would be ambitious for a 1-semester course, such use would be unlikely. The text is clearly geared towards students with no statistics background nor calculus. The text could be used in two styles of course. For 1st year students early chapters on graphs and distributions would be the starting point, omitting later chapters on Chi-square, transformations, distribution-free and size effect chapters. Alternatively, for upper level students the introductory chapters could be bypassed with the latter chapters then covered to completion.\n\nThis text adopts a descriptive style of presentation with topics well and fully explained, much like the “Dummy series”. For this, it may seem a bit “wordy”, but this can well serve students and notably it complements powerpoint slides that are generally sparse on written content. This text could be used as the primary text, for regular lectures, or as reference for a “flipped” class. The e-book videos are an enabling tool if this approach is adopted.\n","created_at":"2018-02-01T18:00:00.000-06:00","updated_at":"2018-02-01T18:00:00.000-06:00"},{"id":1976,"first_name":"Jenna","last_name":"Kowalski","position":"Mathematics Instructor","institution_name":"Anoka-Ramsey Community College","comprehensiveness_rating":5,"comprehensiveness_review":"The text includes the introductory statistics topics covered in a college-level semester course.  An effective index and glossary are included, with functional hyperlinks. ","accuracy_rating":3,"accuracy_review":"The content of this text is accurate and error-free, based on a random sampling of various pages throughout the text.  Several examples included information without formal citation, leading the reader to potential bias and discrimination.  These examples should be corrected to reflect current values of inclusive teaching. ","relevance_rating":4,"relevance_review":"The text contains relevant information that is current and will not become outdated in the near future.  The statistical formulas and calculations have been used for centuries.  The examples are direct applications of the formulas and accurately assess the conceptual knowledge of the reader.  ","clarity_rating":5,"clarity_review":"The text is very clear and direct with the language used.  The jargon does require a basic mathematical and/or statistical foundation to interpret, but this foundational requirement should be met with course prerequisites and placement testing.  Graphs, tables, and visual displays are clearly labeled.  ","consistency_rating":5,"consistency_review":"The terminology and framework of the text is consistent.  The hyperlinks are working effectively, and the glossary is valuable.  Each chapter contains modules that begin with prerequisite information and upcoming learning objectives for mastery.  ","modularity_rating":5,"modularity_review":"The modules are clearly defined and can be used in conjunction with other modules, or individually to exemplify a choice topic.  With the prerequisite information stated, the reader understands what prior mathematical understanding is required to successfully use the module.  ","organization_rating":4,"organization_review":"The topics are presented well, but I recommend placing Sampling Distributions, Advanced Graphs, and Research Design ahead of Probability in the text.  I think this rearranged version of the index would better align with current Introductory Statistics texts.  The structure is very organized with the prerequisite information stated and upcoming learner outcomes highlighted.  Each module is well-defined. ","interface_rating":4,"interface_review":"Adding an option of returning to the previous page would be of great value to the reader.  While progressing through the text systematically, this is not an issue, but when the reader chooses to skip modules and read select pages then returning to the previous state of information is not easily accessible.  ","grammatical_rating":5,"grammatical_review":"No grammatical errors were found while reviewing select pages of this text at random.  ","cultural_rating":3,"cultural_review":"Several examples contained data that were not formally cited.  These examples need to be corrected to reflect current inclusive teaching strategies.  For example, one question stated that “while men are XX times more likely to commit murder than women, …”  This data should be cited, otherwise the information can be interpreted as biased and offensive.  ","overall_rating":9,"overall_review":"An included solutions manual for the exercises would be valuable to educators who choose to use this text.  ","created_at":"2018-03-27T19:00:00.000-05:00","updated_at":"2018-03-27T19:00:00.000-05:00"},{"id":2163,"first_name":"Suhwon","last_name":"Lee","position":"Associate Teaching Professor","institution_name":"University of Missouri","comprehensiveness_rating":5,"comprehensiveness_review":"This book is pretty comprehensive for being a brief introductory book. This book covers all necessary content areas for an introduction to Statistics course for non-math majors. The text book provides an effective index, plenty of exercises, review questions, and practice tests. It provides references and case studies. The glossary and index section is very helpful for students and can be used as a great resource.","accuracy_rating":5,"accuracy_review":"Content appears to be accurate throughout. Being an introductory book, the book is unbiased and straight to the point. The terminology is standard.","relevance_rating":5,"relevance_review":"The content in textbook is up to date. It will be very easy to update it or make changes at any point in time because of the well-structured contents in the textbook.","clarity_rating":5,"clarity_review":"The author does a great job of explaining nearly every new term or concept. The book is easy to follow, clear and concise. The graphics are good to follow. The language in the book is easily understandable. I found most instructions in the book to be very detailed and clear for students to follow.","consistency_rating":5,"consistency_review":"Overall consistency is good. It is consistent in terms of terminology and framework. The writing is straightforward and standardized throughout the text and it makes reading easier.","modularity_rating":5,"modularity_review":"The authors do a great job of partitioning the text and labeling sections with appropriate headings. The table of contents is well organized and easily divisible into reading sections and it can be assigned at different points within the course.","organization_rating":4,"organization_review":"Overall, the topics are arranged in an order that follows natural progression in a statistics course with some exception. They are addressed logically and given adequate coverage.","interface_rating":5,"interface_review":"The text is free of any issues. There are no navigation problems nor any display issues.","grammatical_rating":5,"grammatical_review":"The text contains no grammatical errors.","cultural_rating":4,"cultural_review":"The text is not culturally insensitive or offensive in any way most of time. Some examples might need to consider citing the sources or use differently to reflect current inclusive teaching strategies.","overall_rating":10,"overall_review":"Overall, it's well-written and good recourse to be an introduction to statistical methods. Some materials may not need to be covered in an one-semester course. Various examples and quizzes can be a great recourse for instructor.","created_at":"2018-06-19T19:00:00.000-05:00","updated_at":"2018-06-19T19:00:00.000-05:00"},{"id":2523,"first_name":"Ilgin","last_name":"Sager","position":"Assistant Professor","institution_name":"University of Missouri - St. Louis","comprehensiveness_rating":4,"comprehensiveness_review":"As the title implies, this is a brief introduction textbook. It covers the fundamental of the introductory statistics, however not a comprehensive  text on the subject. A teacher can use this book as the sole text of an introductory statistics. The prose format of definitions and theorems make theoretical concepts accessible to non-math major students. The textbook covers all chapters required in this level course. ","accuracy_rating":5,"accuracy_review":"It is accurate; the subject matter in the examples to be up to date, is timeless and wouldn't need to be revised in future editions; there is no error except a few typographical errors. There are no logic errors or incorrect explanations.","relevance_rating":5,"relevance_review":"This text will remain up to date for a long time since it has timeless examples and exercises, it wouldn't be outdated. The information is presented clearly with a simple way and the exercises are beneficial to follow the information.","clarity_rating":5,"clarity_review":"The material is presented in a clear, concise manner. The text is easy readable for the first time statistics student. ","consistency_rating":4,"consistency_review":"The structure of the text is very consistent. Topics are presented with examples, followed by exercises. Problem sets are appropriate for the level of learner.","modularity_rating":4,"modularity_review":"When the earlier matters need to be referenced, it is easy to find; no trouble reading the book and finding results, it has a consistent scheme. This book is set very well in sections.","organization_rating":5,"organization_review":"The text presents the information in a logical order. ","interface_rating":5,"interface_review":"The learner can easily follow up the material; there is no interface problem.","grammatical_rating":5,"grammatical_review":"There is no logic errors and incorrect explanations, a few typographical errors is just to be ignored.","cultural_rating":5,"cultural_review":"Not applicable for this textbook.","overall_rating":9,"overall_review":null,"created_at":"2019-01-14T13:54:18.000-06:00","updated_at":"2019-01-14T13:54:18.000-06:00"},{"id":2610,"first_name":"Dabrina","last_name":"Dutcher","position":"Assistant Professor","institution_name":"Bucknell University","comprehensiveness_rating":4,"comprehensiveness_review":"This is a reasonably thorough first-semester statistics book for most classes.  It would have worked well for the general statistics courses I have taught in the past but is not as suitable for specialized introductory statistics courses for engineers or business applications. That is OK, they have separate texts for that!  The only sections that feel somewhat light in terms of content are the confidence intervals and ANOVA sections.  Given that these topics are often sort of crammed in at the end of many introductory classes, that might not be problematic for many instructors.  It should also be pointed out that while there are a couple of chapters on probability, this book spends presents most formulas as \"black boxes\" rather than worry about the derivation or origin of the formulas.  The probability sections do not include any significant combinatorics work, which is sometimes included at this level.","accuracy_rating":5,"accuracy_review":"I did not find any errors in the formulas presented but I did not work many end-of-chapter problems to gauge the accuracy of their answers.","relevance_rating":4,"relevance_review":"There isn't much changing in the introductory stats world, so I have no concerns about the book becoming outdated rapidly.  The examples and problems still feel relevant and reasonably modern.  My only concern is that the statistical tool most often referenced in the book are TI-83/84 type calculators.  As students increasingly buy TI-89s or Inspires, these sections of the book may lose relevance faster than other parts.","clarity_rating":4,"clarity_review":"Solid.  The book gives a list of key terms and their definitions at the end of each chapter which is a nice feature.  It also has a formula review at the end of each chapter.  I can imagine that these are heavily used by students when studying!  Formulas are easy to find and read and are well defined.  There are a few areas that I might have found frustrating as a student. For example, the explanation for the difference in formulas for a population vs sample standard deviation is quite weak.  Again, this is a book that focuses on sort of a \"black-box\" approach but you may have to supplement such sections for some students.","consistency_rating":5,"consistency_review":"I did not detect any problems with inconsistent symbol use or switches in terminology. ","modularity_rating":3,"modularity_review":"This low rating should not be taken as an indicator of an issue with this book but would be true of virtually any statistics book.  Different books still use different variable symbols even for basic calculated statistics.  So trying to use a chapter of this book without some sort of symbol/variable cheat-sheet would likely be frustrating to the students. \r\n\r\nHowever, I think it would be possible to skip some chapters or use the chapters in a different order without any loss of functionality.\r\n\r\n ","organization_rating":5,"organization_review":"This book uses a very standard order for the material.  The chapter on regressions comes later than it does in some texts but it doesn't really matter since that chapter never seems to fit smoothly anywhere.\r\n\r\nThere are numerous end of chapter problems, some with answers, available in this book. I'm vacillating on whether these problems would be more useful if they were distributed after each relevant section or are better clumped at the end of the whole chapter.  That might be a matter of individual preference.","interface_rating":5,"interface_review":"I did not detect any problems.","grammatical_rating":5,"grammatical_review":"I found no errors.  However, there were several sections where the punctuation seemed non-ideal.  This did not affect the over-all useability of the book though","cultural_rating":4,"cultural_review":"I'm not sure how well this book would work internationally as many of the examples contain domestic (American) references.  However, I did not see anything offensive or biased in the book. ","overall_rating":9,"overall_review":null,"created_at":"2019-03-04T14:34:08.000-06:00","updated_at":"2019-03-04T14:34:08.000-06:00"},{"id":2971,"first_name":"Alexandra","last_name":"Verkhovtseva","position":"Professor","institution_name":"Anoka-Ramsey Community College","comprehensiveness_rating":4,"comprehensiveness_review":"This is a comprehensive enough text, considering that it is not easy to create a comprehensive statistics textbook. It is suitable for an introductory statistics course for non-math majors. It contains twenty-one chapters, covering the wide range of intro stats topics (and some more), plus the case studies and the glossary. ","accuracy_rating":5,"accuracy_review":"The content is pretty accurate, I did not find any biases or errors. ","relevance_rating":4,"relevance_review":"The book contains fairly recent data presented in the form of exercises, examples and applications. The topics are up-to-date, and appropriate technology is used for examples, applications, and case studies. ","clarity_rating":4,"clarity_review":"The language is simple and clear, which is a good thing, since students are usually scared of this class, and instructors are looking for something to put them at ease. I would, however, try to make it a little more interesting, exciting, or may be even funny. ","consistency_rating":5,"consistency_review":"Consistency is good, the book has a great structure. I like how each chapter has prerequisites and learner outcomes, this gives students a good idea of what to expect. Material in this book  is covered in good detail.","modularity_rating":4,"modularity_review":"The text can be easily divided into sub-sections, some of which can be omitted if needed. The chapter on regression is covered towards the end (chapter 14), but part of it can be covered sooner in the course. ","organization_rating":5,"organization_review":"The book contains well organized chapters that makes reading through easy and understandable. The order of chapters and sections is clear and logical. ","interface_rating":5,"interface_review":"The online version has many functions and is easy to navigate. This book also comes with a PDF version. There is no distortion of images or charts. The text is clean and clear, the examples provided contain appropriate format of data presentation. ","grammatical_rating":5,"grammatical_review":"No grammatical errors found. ","cultural_rating":4,"cultural_review":"The text uses simple and clear language, which is helpful for non-native speakers. I would include more culturally-relevant examples and case studies. Overall, good text. ","overall_rating":9,"overall_review":"In all, this book is a good learning experience. It contains tools and techniques that free and easy to use and also easy to modify for both, students and instructors. I very much appreciate this opportunity to use this textbook at no cost for our students. ","created_at":"2019-06-03T21:21:13.000-05:00","updated_at":"2019-06-03T21:21:13.000-05:00"},{"id":3016,"first_name":"Mamata","last_name":"Marme","position":"Assistant Professor","institution_name":"Augustana College","comprehensiveness_rating":4,"comprehensiveness_review":"This textbook offers a fairly comprehensive summary of what should be discussed in an introductory course in Statistics.   The statistical literacy exercises are particularly interesting.  It would be helpful to have the statistical tables attached in the same package, even though they are available online.","accuracy_rating":5,"accuracy_review":"The terminology and notation used in the textbook is pretty standard. The content is accurate. ","relevance_rating":4,"relevance_review":"The statistical literacy example are up to date but will need to be updated fairly regularly to keep the textbook fresh. The applications within the chapter are accessible and can be used fairly easily over a couple of editions. ","clarity_rating":4,"clarity_review":"The textbook does not necessarily explain the derivation of some of the formulae and this will need to be augmented by the instructor in class discussion.  What is beneficial is that there are multiple ways that a topic is discussed using graphs, calculations and explanations of the results. Statistics textbooks have to cover a wide variety of topics with a fair amount of depth. To do this concisely is difficult.  There is a fine line between being concise and clear, which this textbook does well, and being somewhat dry.  It may be up to the instructor to bring case studies into the readings we are going through the topics rather than wait until the end of the chapter.","consistency_rating":5,"consistency_review":"The textbook uses standard notation and terminology. The heading section of each chapter is closely tied to topics that are covered.  The end of chapter problems and the statistical literacy applications are closely tied to the material covered. ","modularity_rating":4,"modularity_review":"The authors have done a good job treating each chapter as if they stand alone.  The lack of connection to a past reference may create a sense of disconnect between the topics discussed","organization_rating":3,"organization_review":"The text's \"modularity\" does make the flow of the material a little disconnected. If would be better if there was accountability of what a student should already have learnt in a different section.  The earlier material is easy to find but not consistently referred to in the text. ","interface_rating":5,"interface_review":"I had no problem with the interface. The online version is more visually interesting than the pdf version.  ","grammatical_rating":5,"grammatical_review":"I did not see any grammatical errors.","cultural_rating":4,"cultural_review":"I am not sure how to evaluate this. The examples are mostly based on the American experience and the data alluded to mostly domestic. However, I am not sure if that creates a problem in understanding the methodology. ","overall_rating":9,"overall_review":"Overall, this textbook will cover most of the topics in a survey of statistics course. ","created_at":"2019-06-25T14:59:29.000-05:00","updated_at":"2019-06-25T14:59:29.000-05:00"},{"id":4653,"first_name":"Julie","last_name":"Gray","position":"Adjunct Assistant Professor","institution_name":"University of Texas at Arlington","comprehensiveness_rating":5,"comprehensiveness_review":"The textbook is for beginner-level students. The concept development is appropriate--there is always room to grow to high higher level, but for an introduction, the basics are what is needed. This is a well-thought-through OER textbook project by Dr. Lane and colleagues. It is obvious that several iterations have only made it better.","accuracy_rating":5,"accuracy_review":"I found all the material accurate.","relevance_rating":5,"relevance_review":"Essentially, statistical concepts at the introductory level are accepted as universal.  This suggests that the relevance of this textbook will continue for a long time.","clarity_rating":5,"clarity_review":"The book is well written for introducing beginners to statistical concepts. The figures, tables, and animated examples reinforce the clarity of the written text.","consistency_rating":5,"consistency_review":"Yes, the information is consistent; when it is introduced in early chapters it ties in well in later chapters that build on and add more understanding for the topic.","modularity_rating":4,"modularity_review":"The book is well-written with attention to modularity where possible. Due to the nature of statistics, that is not always possible. The content is presented in the order that I usually teach these concepts.","organization_rating":5,"organization_review":"The organization of the book is good, I particularly like the sample lecture slide presentations and the problem set with solutions for use in quizzes and exams. These are available by writing to the author.  It is wonderful to have access to these helpful resources for instructors to use in preparation.","interface_rating":5,"interface_review":"I did not find any interface issues.","grammatical_rating":5,"grammatical_review":"The book is well written. In my reading I did not notice grammatical errors.","cultural_rating":5,"cultural_review":"For this subject and in the examples given, I did not notice any cultural issues.","overall_rating":10,"overall_review":"For the field of social work where qualitative data is as common as quantitative, the importance of giving students the rationale or the motivation to learn the quantitative side is understated. To use this text as an introductory statistics OER textbook in a social work curriculum, the instructor will want to bring in field-relevant examples to engage and motivate students. The field needs data-driven decision making and evidence-based practices to become more ubiquitous than not.  Preparing future social workers by teaching introductory statistics is essential to meet that goal.","created_at":"2021-02-26T15:49:15.000-06:00","updated_at":"2021-02-26T15:49:15.000-06:00"},{"id":4709,"first_name":"Shahar","last_name":"Boneh","position":"Professor","institution_name":"Metropolitan State University of Denver","comprehensiveness_rating":5,"comprehensiveness_review":"The textbook is indeed quite comprehensive.  It can accommodate any style of introductory statistics course.","accuracy_rating":5,"accuracy_review":"The text seems to be statistically accurate.","relevance_rating":4,"relevance_review":"It is a little too extensive, which requires instructors to cover it selectively, and has a potential to confuse the students.","clarity_rating":5,"clarity_review":"It is written clearly.","consistency_rating":4,"consistency_review":"The terminology is fairly consistent.   There is room for some improvement.","modularity_rating":5,"modularity_review":"By the nature of the subject, the topics have to be presented in a sequential and coherent order.  However, the book breaks things down quite effectively.","organization_rating":3,"organization_review":"Some of the topics are interleaved and not presented in the order I would like to cover them.","interface_rating":5,"interface_review":"Good interface.","grammatical_rating":5,"grammatical_review":"The grammar is ok.","cultural_rating":5,"cultural_review":"The book seems to be culturally neutral, and not offensive in any way.","overall_rating":9,"overall_review":"I really liked the simulations that go with the book.  \r\nParts of the book are a little too advanced for students who are learning statistics for the first time.","created_at":"2021-03-26T14:34:57.000-05:00","updated_at":"2021-04-22T16:24:40.000-05:00"},{"id":4720,"first_name":"Audrey","last_name":"Hickert","position":"Assistant Professor","institution_name":"Southern Illinois University Carbondale","comprehensiveness_rating":5,"comprehensiveness_review":"All of the major topics of an introductory level statistics course for social science are covered. Background areas include levels of measurement and research design basics. Descriptive statistics include all major measures of central tendency and dispersion/variation. Building blocks for inferential statistics include sampling distributions, the standard normal curve (z scores), and hypothesis testing sections. Inferential statistics include how to calculate confidence intervals, as well as conduct tests of one-sample tests of the population mean (Z- and t-tests), two-sample tests of the difference in population means (Z- and t-tests), chi square test of independence, correlation, and regression. Doesn’t include full probability distribution tables (e.g., t or Z), but those can be easily found online in many places.","accuracy_rating":5,"accuracy_review":"I did not find any errors or issues of inaccuracy. When a particular method or practice is debated in the field, the authors acknowledge it (and provide citations in some circumstances).","relevance_rating":4,"relevance_review":"Basic statistics are standard, so the core information will remain relevant in perpetuity. Some of the examples are dated (e.g., salaries from 1999), but not problematic.","clarity_rating":4,"clarity_review":"All of the key terms, formulas, and logic for statistical tests are clearly explained. The book sometimes uses different notation than other entry-level books. For example, the variance formula uses \"M\" for mean, rather than x-bar.","consistency_rating":5,"consistency_review":"The explanations are consistent and build from and relate to corresponding sections that are listed in each unit.","modularity_rating":5,"modularity_review":"Modularity is a strength of this text in both the PDF and interactive online format. Students can easily navigate to the necessary sections and each starts with a “Prerequisites” list of other sections in the book for those who need the additional background material. Instructors could easily compile concise sub-sections of the book for readings.","organization_rating":5,"organization_review":"The presentation of topics differs somewhat from the standard introductory social science statistics textbooks I have used before. However, the modularity allows the instructor and student to work through the discrete sections in the desired order.","interface_rating":4,"interface_review":"For the most part the display of all images/charts is good and navigation is straightforward. One concern is that the organization of the Table of Contents does not exactly match the organizational outline at the start of each chapter in the PDF version. For example, sometimes there are more detailed sub-headings at the start of chapter and occasionally slightly different section headings/titles. There are also inconsistencies in section listings at start of chapters vs. start of sub-sections.","grammatical_rating":5,"grammatical_review":"The text is easy to read and free from any obvious grammatical errors.","cultural_rating":5,"cultural_review":"Although some of the examples are outdated, I did not review any that were offensive. One example of an outdated reference is using descriptive data on “Men per 100 Women” in U.S. cities as “useful if we are looking for an opposite-sex partner”.","overall_rating":9,"overall_review":"This is a good introduction level statistics text book if you have a course with students who may be intimated by longer texts with more detailed information. Just the core basics are provided here and it is easy to select the sections you need. It is a good text if you plan to supplement with an array of your own materials (lectures, practice, etc.) that are specifically tailored to your discipline (e.g., criminal justice and criminology). Be advised that some formulas use different notation than other standard texts, so you will need to point that out to students if they differ from your lectures or assessment materials.","created_at":"2021-03-29T20:01:26.000-05:00","updated_at":"2021-03-29T20:01:26.000-05:00"},{"id":4828,"first_name":"Emilio","last_name":"Vazquez","position":"Associate Professor","institution_name":"Trine University","comprehensiveness_rating":5,"comprehensiveness_review":"This appears to be an excellent textbook for an Introductory Course in Statistics. It covers subjects in enough depth to fulfill the needs of a beginner in Statistics work yet is not so complex as to be overwhelming.","accuracy_rating":5,"accuracy_review":"I found no errors in their discussions. Did not work out all of the questions and answers but my sampling did not reveal any errors.","relevance_rating":5,"relevance_review":"Some of the examples may need updating depending on the times but the examples are still relevant at this time.","clarity_rating":5,"clarity_review":"This is a Statistics text so a little dry. I found that the derivation of some of the formulas was not explained. However the background is there to allow the instructor to derive these in class if desired.","consistency_rating":5,"consistency_review":"The text is consistent throughout using the same verbiage in various sections.","modularity_rating":5,"modularity_review":"The text dose lend itself to reasonable reading assignments. For example the chapter (Chapter 3) on Summarizing Distributions covers Central Tendency and its associated components in an easy 20 pages with Measures of Variability making up most of the rest of the chapter and covering approximately another 20 pages. Exercises are available at the end of each chapter making it easy for the instructor to assign reading and exercises to be discussed in class.","organization_rating":5,"organization_review":"The textbook flows easily from Descriptive to Inferential Statistics with chapters on Sampling and Estimation preceding chapters on hypothesis testing","interface_rating":5,"interface_review":"I had no problems with navigation","grammatical_rating":5,"grammatical_review":"All textbooks have a few errors but certainly nothing glaring or making text difficult","cultural_rating":5,"cultural_review":"I saw no issues and I am part of a cultural minority in the US","overall_rating":10,"overall_review":"Overall I found this to be a excellent in-depth overview of Statistical Theory, Concepts and Analysis. The length of the textbook appears to be more than adequate for a one-semester course in Introduction to Statistics. As I no longer teach a full statistics course but simply a few lectures as part of our Research Curriculum, I am recommending this book to my students as a good reference. Especially as it is available on-line and in Open Access.","created_at":"2021-04-23T08:38:16.000-05:00","updated_at":"2021-04-23T08:38:16.000-05:00"},{"id":5165,"first_name":"Professor","last_name":"Sandberg","position":"Professor","institution_name":"Framingham State University","comprehensiveness_rating":5,"comprehensiveness_review":"This text covers all the usual topics in an Introduction to Statistics for college students. In addition, it has some additional topics that are useful.","accuracy_rating":5,"accuracy_review":"I did not find any errors.","relevance_rating":5,"relevance_review":"Some of the examples are dated. And the frequent use of male/female examples need updating in terms of current gender splits.","clarity_rating":5,"clarity_review":"I found it was easy to read and understand and I expect that students would also find the writing clear and the explanations accessible.","consistency_rating":5,"consistency_review":"Even with different authors of chapter, the writing is consistent.","modularity_rating":5,"modularity_review":"The text is well organized into sections making it easy to assign individual topics and sections.","organization_rating":5,"organization_review":"The topics are presented in the usual order. Regression comes later in the text but there is a difference of opinions about whether to present it early with descriptive statistics for bivariate data or later with inferential statistics.","interface_rating":5,"interface_review":"I had no problem navigating the text online.","grammatical_rating":5,"grammatical_review":"The writing is grammatical correct.","cultural_rating":5,"cultural_review":"I saw no issues that would be offensive.","overall_rating":10,"overall_review":"I did like this text. It seems like it would be a good choice for most introductory statistics courses. I liked that the Monty Hall problem was included in the probability section. The author offers to provide an instructor's manual, PowerPoint slides and additional questions. These additional resources are very helpful and not always available with online OER texts.","created_at":"2021-06-29T14:36:19.000-05:00","updated_at":"2021-06-29T14:36:19.000-05:00"},{"id":34656,"first_name":"Terri","last_name":"Torres","position":"professor","institution_name":"Oregon Institute of Technology","comprehensiveness_rating":5,"comprehensiveness_review":"This author covers all the topics that would be covered in an introductory statistics course plus some.  I could imagine using it for two courses at my university, which is on the quarter system.  I would rather have the problem of too many topics rather than too few.","accuracy_rating":5,"accuracy_review":"Yes, Lane is both thorough and accurate.","relevance_rating":5,"relevance_review":"What is covered is what is usually covered in an introductory statistics book.  The only topic I may, given sufficient time, cover is bootstrapping.","clarity_rating":5,"clarity_review":"The book is clear and well-written.  For the trickier topics, simulations are included to help with understanding.","consistency_rating":5,"consistency_review":"All is organized in a way that is consistent with the previous topic.","modularity_rating":5,"modularity_review":"The text is organized in a way that easily enables navigation.","organization_rating":5,"organization_review":"The text is organized like most statistics texts.","interface_rating":5,"interface_review":"Easy navigation.","grammatical_rating":5,"grammatical_review":"I didn't see any grammatical errors.","cultural_rating":5,"cultural_review":"Nothing is included that is culturally insensitive.","overall_rating":10,"overall_review":"The videos that accompany this text are short and easy to watch and understand.  Videos should be short enough to teach, but not so long that they are tiresome.  This text includes almost everything: videos, simulations, case studies---all nicely organized in one spot.  In addition, Lane has promised to send an instructor's manual and slide deck.","created_at":"2023-08-17T15:14:18.000-05:00","updated_at":"2023-08-17T15:14:18.000-05:00"}],"url":"https://open.umn.edu/opentextbooks/textbooks/introduction-to-statistics?locale=es","updated_at":"2026-05-11T02:08:02.000-05:00"},{"id":1078,"title":"Intermediate Statistics with R","edition_statement":null,"volume":null,"copyright_year":2021,"ISBN10":null,"ISBN13":null,"license":"Attribution-NonCommercial","language":"eng","accessibility_statement":null,"accessibility_features":"unknown","description":"Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. Building on the basic statistical thinking emphasized in an introductory course, a second course in statistics at the undergraduate level can explore a large number of statistical methods. This text covers more advanced graphical summaries, One-Way ANOVA with pair-wise comparisons, Two-Way ANOVA, Chi-square testing, and simple and multiple linear regression models. Models with interactions are discussed in the Two-Way ANOVA and multiple linear regression setting with categorical explanatory variables. Randomization-based inferences are used to introduce new parametric distributions and to enhance understanding of what evidence against the null hypothesis “looks like”. Throughout, the use of the statistical software R via Rstudio is emphasized with all useful code and data sets provided within the text. This is Version 3.0 of the book.","contributors":[{"id":5533,"contribution":"Author","primary":true,"corporate":false,"title":null,"first_name":"Mark","middle_name":"C.","last_name":"Greenwood","location":"Montana State University","background_text":"Mark C. Greenwood, Montana State University"}],"subjects":[{"id":35,"name":"Applied","parent_subject_id":7,"call_number":"QA37.3","visible_textbooks_count":48,"url":"https://open.umn.edu/opentextbooks/subjects/applied?locale=es"},{"id":7,"name":"Mathematics","parent_subject_id":null,"call_number":"QA1","visible_textbooks_count":177,"url":"https://open.umn.edu/opentextbooks/subjects/mathematics?locale=es"},{"id":82,"name":"Statistics","parent_subject_id":7,"call_number":"QA273-280","visible_textbooks_count":30,"url":"https://open.umn.edu/opentextbooks/subjects/statistics?locale=es"}],"publishers":[{"id":1056,"url":"https://scholarworks.montana.edu/xmlui/handle/1/3","year":null,"created_at":"2021-09-29T14:27:35.000-05:00","updated_at":"2021-09-29T14:32:16.000-05:00","name":"Montana State University"}],"formats":[{"id":2638,"type":"PDF","url":"https://scholarworks.montana.edu/xmlui/handle/1/2999","price":{"cents":0,"currency_iso":"USD"},"isbn":null}],"rating":"5","textbook_reviews_count":5,"reviews":[{"id":33459,"first_name":"Evidence","last_name":"Matangi","position":"Assistant Professor","institution_name":"Taylor University","comprehensiveness_rating":4,"comprehensiveness_review":"R introduction is concise, data sets introduction is clear with objectives formulating the the questions to answer from the data.\r\nPlots for categorical variables would compliment those for quantitative variables. Grammar of graphics is condensed, more would be better for an introductory textbook. Data wrangling needs to talk about the 5 verbs and also briefly talk about piping. The ASA statement on p-values would be a great addition to this textbook. Briefly introduce loops in R.","accuracy_rating":5,"accuracy_review":"There is some missing information on page 97 on second bullet point for SS_Total.\r\npage 154, Discussion section needs to be distinct as you comment on it earlier for reference.","relevance_rating":5,"relevance_review":"The content is so up-to-date and captivating addressing interesting problems to solve using data.","clarity_rating":4,"clarity_review":"There is need to be intentional in introducing the R packages and its associated jargon. Distinguish between random and fixed effects and also highlight other multiple comparison tests. For example, introduce functions such as favstats and their normal associatives for the five number summary.","consistency_rating":5,"consistency_review":"The flow from chapter to chapter and within chapters is very consistent, leading to the practice problems. The structure of the practice problems is very consistent throughout the textbook.","modularity_rating":5,"modularity_review":"This is a typical set-up for a Statistics textbook. However, there is need to delve deeply on modern statistics topics such as bootstrap resampling and confidence intervals.","organization_rating":5,"organization_review":"Great organization of the topics.","interface_rating":5,"interface_review":"the outputs from R and their associated visuals add more value to the theoretical aspects.","grammatical_rating":5,"grammatical_review":"great communication, it is very clear.","cultural_rating":5,"cultural_review":"Awesome text.","overall_rating":10,"overall_review":"This is a good textbook for intermediate modern statistics. Thank you for this open resource for equality in access to education.","created_at":"2021-11-15T08:14:50.000-06:00","updated_at":"2021-11-15T08:14:50.000-06:00"},{"id":33716,"first_name":"Eric","last_name":"Smith","position":"Professor","institution_name":"Virginia Tech","comprehensiveness_rating":5,"comprehensiveness_review":"The text covers material for a one-semester long class on applied statistics.  I teach a course on biological statistics and it covers most of the areas that I teach in my course.  Some topics I spend some time on are slightly covered – ANCOVA, randomized blocks, mixed models.  Some material on logistic regression might be useful.  However, I can easily add more material using my notes and examples. I found the material to be current, accurate and free of errors.   There is also a nice set of case students for the students to look at to get a sense of what real data is like.  I tend to add two things to my course - first I make a number of \"how do I\" examples with screenshots to help students navigate R and Rstudio.  Second I have students read or try to reproduce analyses in publications so they can get a sense of \"good\" and \"bad\" analyses.  The case studies at the end of the text more or less do this as well so I do not see this as a problem with the text.","accuracy_rating":5,"accuracy_review":"The writing is good quality and I did not find any errors or coding problems.  I like the summary at the end of each chapter about highlights and coding.","relevance_rating":4,"relevance_review":"The book is up to date and the material is quite relevant and current.   It will have to be updated regularly as it is based on R (which changes regularly).  The inclusion of tidyverse is both good and bad in that some students struggle with this topic.","clarity_rating":5,"clarity_review":"The authors have done a good job at explaining statistical analysis and the tools that are available to evaluate data.  It would be nice for the authors to add some comments in the coding to help students recall what is being done although again this something easy for me to add.  Comments might, for example, help explain some of the ggplot coding.  I have found that coding in R is the greatest issue in my course so have numerous examples for them to follow.","consistency_rating":5,"consistency_review":"The text is consistent in terminology and the chapters have a similar structure.  Again, I like the summaries at the end of each chapter.","modularity_rating":5,"modularity_review":"The text is free of significant interface issues, including navigation problems, distortion of images/charts, and any other display features that may distract or confuse the reader.  It is quite easy to find topics and link to specific chapters.  There might be some minor issues moving chapters around as the examples use code and some of the explanations are in other chapters.  This is a rather minor problem though.","organization_rating":5,"organization_review":"I have a slightly different ordering of topics in my course however, it would not be a problem to teach most of the chapters in a different order although there are some obvious chapters that you want to teach in sequence (one-way ANOVA before two-way ANOVA or SLR before MLR).  I tend to teach simple regression and correlation before ANOVA so that I can better explain the estimates that are produced in the ANOVA summary.  Again, this is not a problem as the SLR chapter is relatively independent of the other chapters and can be taught earlier.  Since the general structure of the text is quite sound, I can easily supplement the text with additional examples and material.  The case studies section is especially useful.","interface_rating":5,"interface_review":"The authors used bookdown to prepare the text.  I believe (once the first edition is done) this makes it easy to update, produces a consistent set of graphics and helps with navigation.  To update should also be relatively easy.","grammatical_rating":5,"grammatical_review":"The book has been through a number of editions so the grammar/spelling etc is good.","cultural_rating":5,"cultural_review":"I did not find any issues.","overall_rating":10,"overall_review":"I look forward to using this as the main text the next time I teach my class.   It is a better text than many of the others that involve a first course in statistics using the R package.","created_at":"2022-03-06T09:02:17.000-06:00","updated_at":"2022-03-06T09:02:17.000-06:00"},{"id":34900,"first_name":"Wei","last_name":"Wei","position":"Professor","institution_name":"Metropolitan State University","comprehensiveness_rating":4,"comprehensiveness_review":"The text covered both analysis of variance and regression, the two major areas in statistics, along with some categorical analysis method, Chi-square tests. The author introduced each chapter in detailed approaches. The author even wrote their own R package for some of the analysis. This text did not cover the nonparametric analysis area, which is helpful for analyzing small data. It will also be helpful if the author can offer more exercise questions at the end of each chapter.","accuracy_rating":5,"accuracy_review":"The text was written in very good quality. I did not find any bias on any of the analysis or explanations.","relevance_rating":5,"relevance_review":"The content is very relavant. The author used most up-to-date R functions and packages. Some of the datasets used in the examples are relatively out-dated, but those data work well to help the readers to understand the materials.","clarity_rating":5,"clarity_review":"The text is very clearly written. All the R code and output are provided. The author did very good job of explaining the models and results. The author structured each chapter really well, in terms of model validation, data visualization, statistical analysis, and explanation of the results.","consistency_rating":5,"consistency_review":"The entire text is structured consistently. All the formats of the R code and output are consistent throughout the text.","modularity_rating":4,"modularity_review":"Adding subtitles to each section in each chapter can better guide the readers through the materials. Overall, the text has good modularity with dividing the complicated materials into smaller sections.","organization_rating":5,"organization_review":"The text flows well with the consistent organization for each chapter: introducing the concepts, validating the model , analyzing data with visualizations, and interpreting the results.","interface_rating":5,"interface_review":"There is no significant interface issues throughout the text. Some of the hand-written illustrations/pictures can be in better quality. But those are very minor issues and there are not many of those types of illustrations.","grammatical_rating":5,"grammatical_review":"Very good quality.","cultural_rating":5,"cultural_review":"All the examples used in this text are free of culturally sensitive issues.","overall_rating":10,"overall_review":"This is a very well written text. Enjoyed reading it through. One comment: the author used one of their own R package, catstats, that the readers will need to install it from their GitHub repository. Hope the author will keep this package updated so the readers can continue accessing to it.","created_at":"2024-02-23T18:40:05.000-06:00","updated_at":"2024-02-23T18:40:05.000-06:00"},{"id":35099,"first_name":"Claire Seungeun","last_name":"Lee","position":"Associate Professor","institution_name":"University of Massachusetts Lowell","comprehensiveness_rating":5,"comprehensiveness_review":"The book is comprehensive, covering a wide range of topics in detail. It provides thorough explanations, examples, and practical applications, making it an invaluable resource for readers seeking an in-depth understanding of the subject matter.","accuracy_rating":5,"accuracy_review":"The book presents accurate information on a variety of statistical methods, ensuring that readers receive reliable and up-to-date knowledge.","relevance_rating":5,"relevance_review":"Potential necessary updates to the book will be driven by developments and changes in the different versions of the R language. As R evolves, certain functions, packages, and methodologies may be updated or deprecated, necessitating revisions to ensure the content remains current and accurate.","clarity_rating":5,"clarity_review":"The concept of each statistical method is clearly outlined and thoroughly explained. This ensures that readers gain a solid understanding of the principles behind each method, facilitating better comprehension and application of the techniques discussed.","consistency_rating":5,"consistency_review":"The book maintains a consistent style throughout, employing uniform elements such as R plots, table formats, handwritten methods, and visualizations. This cohesive approach ensures that readers can easily follow and understand the material, regardless of the chapter or section they are reading.","modularity_rating":5,"modularity_review":"Each section is a separate unit, but it builds upon the previous one, ensuring a comprehensive understanding of the material for readers with different levels of expertise.","organization_rating":5,"organization_review":"This book is well-organized, making it a versatile resource suitable for learners at various stages. It is structured to accommodate basic, intermediate, and advanced levels of study.","interface_rating":5,"interface_review":"The book features a user-friendly interface, enhancing the reading experience.","grammatical_rating":5,"grammatical_review":"No grammatical errors are found in this book.","cultural_rating":3,"cultural_review":"As the textbook focuses on statistics, cultural relevance is not emphasized. But I don't think there are some biased examples.","overall_rating":10,"overall_review":"Thank you!","created_at":"2024-06-04T14:39:29.000-05:00","updated_at":"2024-06-04T14:39:29.000-05:00"},{"id":35278,"first_name":"Zhuanzhuan","last_name":"Ma","position":"Assistant Professor of Statistics","institution_name":"The University of Texas Rio Grande Valley","comprehensiveness_rating":5,"comprehensiveness_review":"The textbook covers a wide range of statistical methods and techniques, including data wrangling, basic hypothesis testing, one-way and two-way ANOVA, permutation tests, chi-square tests, simple and multiple linear regression, as well as bootstrapping​. These are core concepts that any student of intermediate statistics would need to master. The integration of R, specifically the tidyverse and ggplot, makes the book comprehensive in terms of modern statistical software use. It provides detailed explanations and R code for each topic, helping students learn both the statistical method and its implementation​.","accuracy_rating":5,"accuracy_review":"The accuracy of Intermediate Statistics with R by Mark C. Greenwood appears to be high, particularly in its explanations of statistical concepts and its use of R for data analysis. The real-world examples and datasets used in the book help ground the theory in practical applications. These examples are representative of common statistical scenarios, ensuring that the material is not only accurate but also applicable to real-world data analysis. The accuracy of the textbook is strong in both its statistical content and its implementation in R. The author’s continual updates help maintain the relevance and correctness of the material, especially given the evolving nature of the R programming language.","relevance_rating":5,"relevance_review":"Intermediate Statistics with R is highly relevant due to its alignment with modern statistical practices, its use of widely-adopted software, and its focus on practical, real-world applications. The skills and methods taught in the book are not only critical for academic success but are also directly transferable to professional data analysis roles.","clarity_rating":5,"clarity_review":"The clarity of Intermediate Statistics with R is one of its notable strengths. The text is written in a straightforward, accessible style, making it easier for students transitioning from introductory to intermediate-level statistics to follow along and engage with more complex topics. The book includes diagrams, flowcharts, and plots that visually clarify complex concepts, particularly when discussing statistical models or hypothesis testing. These visuals help break down information that might be difficult to grasp from text alone​. Each chapter concludes with a summary of key points and practice problems, which helps reinforce the material. These summaries distill the chapter’s content into its most important takeaways, making it easier for students to review and retain information.","consistency_rating":5,"consistency_review":"The book is internally consistent in its use of terminology, structure, and instructional approach. The book follows a predictable structure in each chapter, starting with a theoretical introduction, followed by practical implementation in R, and ending with summaries and practice problems, which creates a cohesive learning experience. The integration of R, particularly through the tidyverse package and ggplot for data visualization, remains uniform throughout, allowing students to apply similar coding logic across different statistical problems. Additionally, the progression of topics is logical, moving from basic to more complex methods, ensuring that concepts build upon one another smoothly. The use of visual aids, R code snippets, and consistent pedagogical tools like summaries and exercises further reinforces this consistency, making the text an effective and coherent resource for intermediate statistics students.","modularity_rating":5,"modularity_review":"The book is highly modular, making it easy to divide into smaller reading sections that can be assigned at various points in a course. Each chapter is broken down into distinct sections with clear subheadings, allowing instructors to assign specific topics such as hypothesis testing, ANOVA, or regression independently without requiring students to read entire chapters at once. The text is not overly self-referential, so while there is a logical progression in the order of topics, each section can stand on its own, enabling flexible realignment with different subunits of a course. This modularity ensures that instructors can adapt the material to their specific course structure, assigning sections based on the needs of their students without causing disruption or confusion. Moreover, each section includes self-contained explanations, examples, and R code, making it straightforward for readers to engage with the material in smaller, manageable portions.","organization_rating":5,"organization_review":"The book is logical and clear, with topics presented in a structured progression that guides the reader through increasingly complex statistical methods. The book begins by reviewing foundational concepts, such as summary statistics and basic hypothesis testing, before moving into more advanced techniques like ANOVA, regression, and bootstrapping. This step-by-step approach allows students to build on their knowledge incrementally, ensuring they grasp the fundamental ideas before tackling more sophisticated models. Each chapter flows naturally into the next, and within each chapter, concepts are introduced with clear explanations, followed by practical R examples and visualizations. The consistent chapter structure, which includes summaries, key R code, and practice problems, reinforces the logical presentation of topics. This clear and methodical organization makes the text easy to follow, ensuring that readers can engage with the material without confusion.","interface_rating":5,"interface_review":"The book is free from significant issues, providing a smooth and user-friendly experience for readers. The text is well-formatted, with clear navigation and distinct sections, making it easy for readers to find specific topics or chapters. Additionally, hyperlinks to external resources or references function properly, and the layout—whether in print or digital form—remains organized and easy to follow, avoiding distractions or confusion for the reader. This well-executed interface design helps enhance the learning experience by keeping the focus on the content.","grammatical_rating":5,"grammatical_review":"The text contains no grammatical errors.","cultural_rating":5,"cultural_review":"The real data set in the textbook are inclusive of a variety of backgrounds and disciplinary.","overall_rating":10,"overall_review":"I will use this book for my statistical methods class.","created_at":"2024-10-21T17:46:01.000-05:00","updated_at":"2024-10-21T17:46:01.000-05:00"}],"url":"https://open.umn.edu/opentextbooks/textbooks/intermediate-statistics-with-r?locale=es","updated_at":"2026-05-11T02:05:42.000-05:00"},{"id":1038,"title":"PSYC 2200: Elementary Statistics for the Behavioral and Social Sciences","edition_statement":null,"volume":null,"copyright_year":2021,"ISBN10":null,"ISBN13":null,"license":"Attribution-ShareAlike","language":"eng","accessibility_statement":null,"accessibility_features":"unknown","description":"Welcome to behavioral statistics, a statistics textbook for social science majors!","contributors":[{"id":5472,"contribution":"Author","primary":true,"corporate":false,"title":null,"first_name":"Michelle","middle_name":null,"last_name":"Oja","location":"Taft College","background_text":"Michelle Oja, Taft College"}],"subjects":[{"id":35,"name":"Applied","parent_subject_id":7,"call_number":"QA37.3","visible_textbooks_count":48,"url":"https://open.umn.edu/opentextbooks/subjects/applied?locale=es"},{"id":7,"name":"Mathematics","parent_subject_id":null,"call_number":"QA1","visible_textbooks_count":177,"url":"https://open.umn.edu/opentextbooks/subjects/mathematics?locale=es"},{"id":82,"name":"Statistics","parent_subject_id":7,"call_number":"QA273-280","visible_textbooks_count":30,"url":"https://open.umn.edu/opentextbooks/subjects/statistics?locale=es"}],"publishers":[{"id":1016,"url":"https://libretexts.org/","year":null,"created_at":"2021-08-16T11:36:55.000-05:00","updated_at":"2021-08-16T20:44:49.000-05:00","name":"LibreTexts"}],"formats":[{"id":2540,"type":"PDF","url":"https://stats.libretexts.org/Courses/Taft_College/PSYC_2200%3A_Elementary_Statistics_for_Behavioral_and_Social_Sciences_(Oja)","price":{"cents":0,"currency_iso":"USD"},"isbn":null},{"id":2541,"type":"Online","url":"https://stats.libretexts.org/Courses/Taft_College/PSYC_2200%3A_Elementary_Statistics_for_Behavioral_and_Social_Sciences_(Oja)","price":{"cents":0,"currency_iso":"USD"},"isbn":null},{"id":2542,"type":"Hardcopy","url":"https://libretexts.org/bookstore/order?stats-21991","price":{"cents":0,"currency_iso":"USD"},"isbn":null}],"rating":"5","textbook_reviews_count":1,"reviews":[{"id":35594,"first_name":"Rebeca","last_name":"Petean","position":"Psychology Instructor","institution_name":"Portland State University","comprehensiveness_rating":5,"comprehensiveness_review":"This textbook does a great job walking students through the “big picture” of statistics for the behavioral and social sciences. It starts with the basics—what data look like and how to describe them—then builds step-by-step toward more complex topics like hypothesis testing, t-tests, ANOVAs, correlations, and chi-square. I appreciate that each test has its own dedicated section, which makes it easy for students (and instructors) to find exactly what they need. The coverage feels complete for an undergraduate course, though it might be even stronger with a few more real-world examples drawn from a wider range of social science research.","accuracy_rating":5,"accuracy_review":"From what I’ve reviewed, the content is accurate, clear, and in line with standard statistical practices. The explanations match what’s taught in most social science statistics courses, and the formulas and definitions are correct. The tone is approachable without oversimplifying, which helps students build confidence while still learning the “right way” to do things. I didn’t spot any errors or outdated information.","relevance_rating":5,"relevance_review":"This is the kind of material that doesn’t go out of style anytime soon. Core statistical concepts—like calculating a mean, running a t-test, or interpreting an ANOVA—are timeless in the behavioral and social sciences. Because of that, the book will stay relevant for years, with any future updates likely just needing fresh examples or datasets. Including APA style is a nice touch too, since it gives students a head start on professional reporting standards they’ll need beyond the classroom.","clarity_rating":5,"clarity_review":"The writing is clear and approachable, which is especially important for a subject that can feel intimidating to students. The explanations break down statistical concepts step-by-step, and the use of examples makes it easier to connect abstract ideas to real-world situations. Technical terms are introduced with enough context so that students aren’t left guessing, and the structure of each section helps reinforce understanding before moving on.","consistency_rating":5,"consistency_review":"The text uses terminology and notation consistently from start to finish, which really helps prevent confusion. Once a concept is introduced—like “mean difference” or “p-value”—it’s applied in the same way throughout the book. The framework and style stay steady too, so students can focus on learning the material rather than adjusting to new formats or language in each chapter.","modularity_rating":5,"modularity_review":"The book is easy to break into smaller chunks for teaching. Each topic stands well on its own, so an instructor can assign readings in different orders or skip sections without losing flow. The logical division into units and subtopics also means students can revisit a single concept—like regression or chi-square—without having to reread unrelated material. That flexibility makes it a great fit for different course structures.","organization_rating":5,"organization_review":"The book’s organization feels natural and easy to follow. It starts with foundational concepts before moving into more complex analyses, which helps students build confidence step-by-step. The chapters are well-sequenced, and the transitions between topics are smooth, so it never feels like a sudden jump. This structure works well for both teaching and self-paced learning.","interface_rating":5,"interface_review":"The online format is clean and easy to navigate. Headings, tables, and figures display clearly without distortion, and the layout makes it simple to jump between sections","grammatical_rating":5,"grammatical_review":"Sentences are well-constructed, punctuation is correct, and the tone balances professionalism with accessibility. The grammar supports the clarity of the content.","cultural_rating":5,"cultural_review":"The text is culturally neutral and free from bias or insensitive language. While the examples are clear and accessible, there’s room to incorporate a broader range of cultural contexts and research scenarios so that students from different backgrounds can see themselves reflected in the material. Even small additions—like datasets from diverse communities or case studies across various cultural settings—could make the book feel even more inclusive.","overall_rating":10,"overall_review":"Overall, this is a well-written, well-structured, and student-friendly statistics textbook. It balances clarity with depth, making complex topics approachable without oversimplifying. With a few more diverse, real-world examples, it could be an even stronger resource for today’s classrooms. I would recommend it to colleagues looking for an open, adaptable, and reliable text for behavioral and social science statistics courses.","created_at":"2025-08-14T01:25:49.000-05:00","updated_at":"2025-08-14T01:25:49.000-05:00"}],"url":"https://open.umn.edu/opentextbooks/textbooks/psyc-2200-elementary-statistics-for-the-behavioral-and-social-sciences?locale=es","updated_at":"2026-05-11T02:06:33.000-05:00"},{"id":447,"title":"Statistical Inference For Everyone","edition_statement":null,"volume":null,"copyright_year":2017,"ISBN10":null,"ISBN13":null,"license":"Attribution-ShareAlike","language":"eng","accessibility_statement":null,"accessibility_features":"unknown","description":"This is a new approach to an introductory statistical inference textbook, motivated by probability theory as logic. It is targeted to the typical Statistics 101 college student, and covers the topics typically covered in the first semester of such a course. It is freely available under the Creative Commons License, and includes a software library in Python for making some of the calculations and visualizations easier.","contributors":[{"id":4163,"contribution":"Author","primary":true,"corporate":false,"title":null,"first_name":"Brian","middle_name":null,"last_name":"Blais","location":"Bryant University","background_text":"Brian Blais professor of Science and Technology, Bryant University and a research professor at the Institute for Brain and Neural Systems, Brown University."}],"subjects":[{"id":35,"name":"Applied","parent_subject_id":7,"call_number":"QA37.3","visible_textbooks_count":48,"url":"https://open.umn.edu/opentextbooks/subjects/applied?locale=es"},{"id":7,"name":"Mathematics","parent_subject_id":null,"call_number":"QA1","visible_textbooks_count":177,"url":"https://open.umn.edu/opentextbooks/subjects/mathematics?locale=es"},{"id":82,"name":"Statistics","parent_subject_id":7,"call_number":"QA273-280","visible_textbooks_count":30,"url":"https://open.umn.edu/opentextbooks/subjects/statistics?locale=es"}],"publishers":[{"id":360,"url":"https://github.com/bblais/Statistical-Inference-for-Everyone","year":null,"created_at":"2018-09-07T12:22:39.000-05:00","updated_at":"2020-01-02T22:39:17.000-06:00","name":"Brian Blais"}],"formats":[{"id":619,"type":"PDF","url":"https://github.com/bblais/Statistical-Inference-for-Everyone/raw/master/Statistical%20Inference%20For%20Everyone.pdf","price":{"cents":0,"currency_iso":"USD"},"isbn":null},{"id":620,"type":"Online","url":"https://github.com/bblais/Statistical-Inference-for-Everyone","price":{"cents":0,"currency_iso":"USD"},"isbn":null}],"rating":"4.5","textbook_reviews_count":4,"reviews":[{"id":2122,"first_name":"Adam","last_name":"Molnar","position":"Assistant Professor","institution_name":"Oklahoma State University","comprehensiveness_rating":2,"comprehensiveness_review":"This book is not a comprehensive introduction to elementary statistics, or even statistical inference, as the author Brian Blais deliberately chose not to cover all topics of statistical inference. For example, the term matched pairs never appears; neither do Type I or Type II error. The Student's t distribution gets much less attention than in almost every other book; the author offers a rarely used standard-deviation change (page 153) as a way to keep things Gaussian.\nThe author justifies the reduced topic set by calling typical \"traditional\" approaches flawed in the first pages of text, the Proposal. Instead, Blais tries to develop statistical inference from logic, in a way that might be called Bayesian inference. Other books have taken this approach, more than just Donald Berry's book mentioned on page 32. [For more references, see the ICOTS6 paper by James Albert at https://iase-web.org/documents/papers/icots6/3f1_albe.pdf ] None  of those books are open-resource, though; an accurate, comprehensive textbook would have potential. This PDF does not contain that desired textbook, however. As mentioned below under accuracy, clarity, and structure, there are too many missing elements, including the lack of an index. As I read, this PDF felt more like a augmented set of lecture notes than a textbook which stands without instructor support. It's not good enough. (For more on this decision, see the other comments at the end.)","accuracy_rating":2,"accuracy_review":"The only non-troubling number of errors in a textbook is zero, but this book has many more than that. In the version I read from the Minnesota-hosted website, my error list includes not defining quartiles from the left (page 129), using ICR instead of IQR (page 133), misstating the 68-95-99 rule as 65-95-99 (page 134), flipping numbers in the combination of the binomial formula (page 232), repeating Figure C-2 as Figure C-1 (page 230), and titling section 2.6 \"Monte Hall\" instead of \"Monty Hall\". Infuriatingly, several of these mistakes are correct elsewhere in the book - Monty Hall in section 5.4, the binomial formula in the main text, and 68-95-99 on page 142. \nI'm also annoyed that some datasets have poor source citations, such as not indicating Fisher's iris data on page 165 and calling something \"student measurements during a physics lab\" on page 173.","relevance_rating":4,"relevance_review":"Because there are so many gaps, including full support for computer presentation, it would be easy to update completed sections as needed, such as when Python becomes less popular.\n","clarity_rating":2,"clarity_review":"Quality of the prose is fine, but many jargon terms are not well defined. Students learning a subject need clear definitions, but they don't appear. In my notes, I see exclusive (page 36), conditioning (page 40), complement (used on page 40 but never appears in the text), posterior (page 54), correlation (page 55), uniform distribution (page 122), and Greek letters for which the reference to a help table appears on page 140, but Greek letters have appeared earlier. Additionally, several important terms receive insufficient or unusual definitions, including labeling summary description of data as inference (page 34), mutually exclusive (page 36) versus independence (page 43), and plus/minus (page 146, as this definition of +/- applies in lab bench science but not social sciences). I appreciate that the author is trying to avoid calculus with \"area under the curve\" on page 127, but there's not enough written for a non-calculus student to understand how these probabilities are calculated. To really understand posterior computation, a magical computer and a few graphs aren't good enough.","consistency_rating":5,"consistency_review":"Internal consistency to Bayesian inference is quite strong; many of the examples repeat the steps of Bayes' Recipe. This is not a concern.","modularity_rating":3,"modularity_review":"The book needs to be read in linear order, like most statistics books, but that's not necessarily a negative thing. Dr. Blais is trying to take the reader through a structured development of Bayesian inference, which has a single path. There are a few digressions, such as fallacies about probability reasoning, but the book generally maintains a single path from chapters 1 to at least 7. Most sections are less than 10 pages and don't involve lots of self-references. Although I rated reorganization possibility as low, due to the near-impossibility of realigning the argument, I consider it harsh to penalize the book for this.","organization_rating":2,"organization_review":"There isn't enough structure for a textbook; this feels more like a set of augmented lecture notes that a book for guided study. I mentioned poor definitions under \"Clarity\", so let me add other topics here. The most frustrating structural problem for me is the presentation of the fundamental idea of Bayesian inference, posterior proportional to prior * likelihood. The word prior first appears on page 48, but receives no clear definition until a side-note on page 97. The word posterior first appears on page 53. Despite this, the fundamental equation is never written with all three words in the correct places until page 154. That's way, way too late. The three key terms should have been defined around page 50 and drilled throughout all the sections.\nThe computer exercises also have terrible structure. The first section with computer exercises, section 2.9 on page 72, begins with code. The reader has no idea about the language, package, or purpose of these weird words in boxes. The explanation about Python appears as Appendix A, after all the exercises. It would not have taken much to explain Python and the purpose of the computer exercises in Chapter 1 or 2, but it didn't happen. A classroom instructor could explain this in class, but the Open Resource Project doesn't provide an instructor with every book. Like the other things mentioned, the structure around computing is insufficient. \n","interface_rating":5,"interface_review":"I had no problems navigating through the chapters. Images look fine as well.\n","grammatical_rating":5,"grammatical_review":"Grammar and spelling are good. I only spotted one typographical error, \"posterier\" on page 131, and very few awkward sentences.","cultural_rating":4,"cultural_review":"This is a US-centered book, since it refers to the \"standard deck\" of playing cards on page 36 as the US deck; other places like Germany have different suits. The book also uses \"heads\" and \"tails\" for coins, while other countries such as Mexico use different terms. I wouldn't call this a major problem, however; the pictures and diagrams make the coins and cards pretty clear. There aren't many examples involving people, so there's little scope for ethnicities and backgrounds. ","overall_rating":7,"overall_review":"On Brian Blais's webpage for the book, referenced only in Appendix A for some reason, he claims that this book is targeted to the typical Statistics 101 college student. It is NOT. Typical college students need much more support than what this book offers - better structure, better scaffolding, more worked examples, support for computing. What percentage of all college students would pick up Python given the contents presented here? My prior estimate would be 5%. Maybe students at Bryant university, where Pre-Calculus is the lowest math course offered, have a higher Python rate, but the bottom 20% of my students at Oklahoma State struggle with order of operations and using the combinations formula. They would need massive support, and Oklahoma State enrolls above-average college students. This book does not have massive support - or much at all.\nThis makes me sad, because I've argued that we should teach hypothesis testing through credible intervals because I think students will understand the logic better than the frequentist philosophical approach. In 2014, I wrote a guest blog post [http://www.culturalcognition.net/blog/2014/9/5/teaching-how-to-teach-bayess-theorem-covariance-recognition.html] on teaching Bayes' Rule. I would value a thorough book that might work for truly typical students, but for the students in my everyone, this won't work.","created_at":"2018-05-21T19:00:00.000-05:00","updated_at":"2018-05-21T19:00:00.000-05:00"},{"id":2553,"first_name":"Jimmy","last_name":"Chen","position":"Assistant Professor","institution_name":"Bucknell University","comprehensiveness_rating":5,"comprehensiveness_review":"As far as Statistical Inference goes, the author has done a great job covering the essential topics. The breadth and the depth of the content are are well balanced. I believe this book can be a great supplemental material for any statistics or probability course. Students would have no problems studying this book themselves because the author has explained concepts clearly and provided ample examples. ","accuracy_rating":5,"accuracy_review":"I think the content is fine. Examples, illustration, and computer codes are all very helpful for the readers to understand the content.","relevance_rating":5,"relevance_review":"The relevance of the book is great. Most supporting examples would be easily relatable to most students. Most statistics or probability concepts discussed in the book are timeless. Detailed computer codes make it easy for verification.","clarity_rating":5,"clarity_review":"The author has explained concepts very well. The flow of the text and examples are great and thoughtful, make it very easy to flow.","consistency_rating":5,"consistency_review":"The consistency of the text is great.","modularity_rating":5,"modularity_review":"The modularity of the text is great. I could easily adopt the entire book or use only certain sections of the book for my teaching. ","organization_rating":5,"organization_review":"The topics in the text are presented in a logical, clear fashion. ","interface_rating":5,"interface_review":"The layout of the text are clear and easily readable. Imagines, charts, and tables are clear and concise. Very easy to follow.","grammatical_rating":5,"grammatical_review":"The text contains no grammatical errors.","cultural_rating":5,"cultural_review":"The text is not culturally insensitive or offensive in any way.","overall_rating":10,"overall_review":null,"created_at":"2019-01-26T20:40:15.000-06:00","updated_at":"2019-01-26T20:40:15.000-06:00"},{"id":4414,"first_name":"Kenese","last_name":"Io","position":"PhD candidate","institution_name":"Colorado State University","comprehensiveness_rating":4,"comprehensiveness_review":"The book illustrates a very pragmatic approach with little theoretical application.  I would recommend this text to anyone who is teaching applied stats at an early level.","accuracy_rating":5,"accuracy_review":"The book is accurate with a number of very helpful examples for new researchers.  The examples provide examples of code for students to use and draw from as they execute their own examples.  They also provide examples with commonly used datasets which is very helpful for some students who may be working on their final projects as an undergraduate or homework assignments as a first year graduate student.","relevance_rating":5,"relevance_review":"The book is problem or problem set oriented which will allow the book to maintain its longevity.  The examples offer analysis of old data but this is very helpful as instructors can assign similar problem sets with new datasets while the students have an excellent tool to rely on.","clarity_rating":4,"clarity_review":"The book is generally clear but given that it is problem oriented some of the theoretical background is scarce and leaves a bit to be desired.  Nevertheless the examples really allow for an immersive experience.","consistency_rating":5,"consistency_review":"The book does a great job of following a clear formula of historical background/ brief theoretical walkthrough/ long examples that force you engage critically with the assignment.","modularity_rating":5,"modularity_review":"The book is very easy to assign as the text quickly jumps to examples of matlab code that will draw students to engage with it.  I can imagine students constantly flipping between their own code and the text to help simplify analysis or execute their code.","organization_rating":4,"organization_review":"The book is organized relatively well. I would have liked to see a few of the later chapters earlier likt the common tests for statistical significance but it generally goes from broader to more narrow perspectives.","interface_rating":5,"interface_review":"The graphs and code examples are laid  out well and the text works great in acrobat reader.","grammatical_rating":5,"grammatical_review":"No errors","cultural_rating":4,"cultural_review":"The text does not offer any critical analysis here but this is due to maintaining general examples.  I think an instructor could easily assign more critical assignments that rely on the intuition laid out in the book.","overall_rating":9,"overall_review":null,"created_at":"2020-11-30T18:47:01.000-06:00","updated_at":"2020-11-30T18:47:01.000-06:00"},{"id":35351,"first_name":"Swetank","last_name":"Mohan","position":"Lecturer 1","institution_name":"The University of Texas Rio Grande Valley","comprehensiveness_rating":5,"comprehensiveness_review":"The textbook covers a wide range of topics in statistical inference, from foundational probability concepts through Bayesian concepts.","accuracy_rating":5,"accuracy_review":"Overall, the content for now appear accurate and grounded in standard statistical theory and practice.","relevance_rating":5,"relevance_review":"The material might be relevant for students and practitioners of statistics.","clarity_rating":5,"clarity_review":"The writing is generally accessible, with step-by-step explanations","consistency_rating":5,"consistency_review":"The text maintains a consistent tone and approach throughout.","modularity_rating":5,"modularity_review":"The text is well-structured into chapters.","organization_rating":5,"organization_review":"Chapters flow logically from basic probability concepts to inference techniques","interface_rating":5,"interface_review":"the textbook’s interface is clear, with readable fonts, good spacing, and functional navigation tools like a table of contents","grammatical_rating":5,"grammatical_review":"Grammar is largely correct","cultural_rating":5,"cultural_review":"language is culturally neutral and inclusive","overall_rating":10,"overall_review":"This textbook strikes a nice balance between theoretical rigor and approachable teaching style","created_at":"2024-12-16T15:43:23.000-06:00","updated_at":"2024-12-16T15:43:23.000-06:00"}],"url":"https://open.umn.edu/opentextbooks/textbooks/statistical-inference-for-everyone?locale=es","updated_at":"2026-05-11T02:07:22.000-05:00"},{"id":746,"title":"Statistical Thinking for the 21st Century","edition_statement":null,"volume":null,"copyright_year":2018,"ISBN10":null,"ISBN13":null,"license":"Attribution-NonCommercial","language":"eng","accessibility_statement":null,"accessibility_features":"unknown","description":"Statistical thinking is a way of understanding a complex world by describing it in relatively simple terms that nonetheless capture essential aspects of its structure, and that also provide us some idea of how uncertain we are about our knowledge. The foundations of statistical thinking come primarily from mathematics and statistics, but also from computer science, psychology, and other fields of study.","contributors":[{"id":4926,"contribution":"Author","primary":true,"corporate":false,"title":null,"first_name":"Russell","middle_name":"A.","last_name":"Poldrack","location":"Stanford University","background_text":"Russell A. Poldrack"}],"subjects":[{"id":35,"name":"Applied","parent_subject_id":7,"call_number":"QA37.3","visible_textbooks_count":48,"url":"https://open.umn.edu/opentextbooks/subjects/applied?locale=es"},{"id":82,"name":"Statistics","parent_subject_id":7,"call_number":"QA273-280","visible_textbooks_count":30,"url":"https://open.umn.edu/opentextbooks/subjects/statistics?locale=es"},{"id":42,"name":"Psychology","parent_subject_id":9,"call_number":"BF121","visible_textbooks_count":51,"url":"https://open.umn.edu/opentextbooks/subjects/psychology?locale=es"}],"publishers":[{"id":725,"url":"http://statsthinking21.org/index.html#why-does-this-book-exist","year":null,"created_at":"2019-07-21T09:34:13.000-05:00","updated_at":"2020-08-30T15:56:40.000-05:00","name":"Russell Poldrack"}],"formats":[{"id":1274,"type":"Online","url":"https://statsthinking21.github.io/statsthinking21-core-site/","price":{"cents":0,"currency_iso":"USD"},"isbn":null},{"id":1275,"type":"PDF","url":"https://statsthinking21.github.io/statsthinking21-core-site/","price":{"cents":0,"currency_iso":"USD"},"isbn":null},{"id":1276,"type":"eBook","url":"https://statsthinking21.github.io/statsthinking21-core-site/","price":{"cents":0,"currency_iso":"USD"},"isbn":null}],"rating":"4","textbook_reviews_count":2,"reviews":[{"id":3414,"first_name":"Larry","last_name":"Leemis","position":"Professor","institution_name":"William \u0026 Mary","comprehensiveness_rating":3,"comprehensiveness_review":"This review concerns an incomplete draft of the book dated 12-9-2019.  Although this appears to be a textbook for a college course directed\r\nat psychology students enrolled in a statistics course, there are no homework exercises, nor is their an index at this point in time.\r\nThere are numerous omissions, typographical errors, etc. in this draft. The book appears to have potential as a textbook once completed. The \r\nauthor is using R as a platform for data visualization and modeling, which is an appropriate choice for the material being presented.","accuracy_rating":1,"accuracy_review":"The draft of the textbook contains numerous errors and omissions.","relevance_rating":4,"relevance_review":"The author uses up-to-date examples.  He tends to prefer the use of packages in R rather than the functions in the base language (for example, using\r\na tibble rather than a standard data frame).  The content is modular, which means that chapters can be easily updated.\r\n","clarity_rating":4,"clarity_review":"In the chapters which are complete, the presentation is lucid.","consistency_rating":4,"consistency_review":"Statistical methods tend to have an ad hoc nature, which means that it is difficult to have notation that remains consistent throughout.\r\nChapters are often paired, with an introductory chapter covering the statistical method followed by a chapter on the implementation in R,\r\nwhich is helpful.  The author does well in presenting statistical concepts without the benefit of a strong mathematical foundation.\r\n","modularity_rating":3,"modularity_review":"The chapters are largely self-contained.  It will be difficult for students who are new to the R language to generalize their understanding\r\nof the functions to a more general setting from just an example or two. \r\n","organization_rating":4,"organization_review":"The pairing of the chapters (for example, Chapter 12 is titled Sampling and Chapter 13 is titled Sampling in R) should be helpful\r\nfor the students in order to separate the statistical technique from its implementation.\r\n","interface_rating":4,"interface_review":"The author appears to have written the draft in LaTeX.  Some of the font sizes on labels on figures should be adjusted so that they are\r\nclose to the font size in the text when possible.  Lines of code currently run off of the page, which I assume will be corrected in\r\nthe future.","grammatical_rating":4,"grammatical_review":"The author is clearly a good writer.  Once the draft gets completed, the typos removed, exercises written, and an index generated, this\r\nbook would be appropriate as a college-level statistics text for psychology students.\r\n","cultural_rating":4,"cultural_review":"The author does not limit the discussion to just psychology applications.  Examples ranging from 2017 election results to Steph Curry free throw\r\nresults are included.\r\n","overall_rating":7,"overall_review":null,"created_at":"2019-12-20T11:35:44.000-06:00","updated_at":"2019-12-20T11:35:44.000-06:00"},{"id":35605,"first_name":"Sage","last_name":"Bushman","position":"Faculty Tutor","institution_name":"Rogue Community College","comprehensiveness_rating":5,"comprehensiveness_review":"This textbook would be very beneficial for an introductory statistics student. ","accuracy_rating":5,"accuracy_review":"Based on my experience teaching and tutoring statistics, the accuracy of this textbook is nothing to worry over. The information is true and clear. ","relevance_rating":5,"relevance_review":"This text is up-to-date with statistical analysis of the 21st century being the main focus. It will mot be difficult to update the information in this textbook if ever necessary. ","clarity_rating":5,"clarity_review":"This is a great textbook for students providing clear instruction for those who are newer to statistics. ","consistency_rating":5,"consistency_review":"The terminology and subject matter is consistent throughout the entirety of this textbook. ","modularity_rating":5,"modularity_review":"You can easily break this textbook down into smaller segments for both the students and teachers. ","organization_rating":5,"organization_review":"This textbook is organized in such a way to provide ease of use by both students and teachers. ","interface_rating":5,"interface_review":"This text is free of significant interface issues that could cause readability errors or difficulties that could cause student and teacher confusion. ","grammatical_rating":5,"grammatical_review":"I did not come across any grammatical errors while skimming through this textbook. ","cultural_rating":5,"cultural_review":"I found this text to be culturally sensitive and provided a variety of examples. ","overall_rating":10,"overall_review":null,"created_at":"2025-08-25T15:10:12.000-05:00","updated_at":"2025-08-25T15:10:12.000-05:00"}],"url":"https://open.umn.edu/opentextbooks/textbooks/statistical-thinking-for-the-21st-century?locale=es","updated_at":"2026-05-11T02:07:34.000-05:00"},{"id":737,"title":"Answering questions with data: Introductory Statistics for Psychology Students","edition_statement":null,"volume":null,"copyright_year":2018,"ISBN10":null,"ISBN13":null,"license":"Attribution-ShareAlike","language":"eng","accessibility_statement":"","accessibility_features":"","description":"This is a free textbook teaching introductory statistics for undergraduates in Psychology. This textbook is part of a larger OER course package for teaching undergraduate statistics in Psychology, including this textbook, a lab manual, and a course website. All of the materials are free and copiable, with source code maintained in Github repositories.","contributors":[{"id":4914,"contribution":"Author","primary":true,"corporate":false,"title":null,"first_name":"Matthew","middle_name":"J. C.","last_name":"Crump","location":"Brooklyn College - City University of New York","background_text":"Matthew J. C. Crump, Brooklyn College - City University of New York"}],"subjects":[{"id":35,"name":"Applied","parent_subject_id":7,"call_number":"QA37.3","visible_textbooks_count":48,"url":"https://open.umn.edu/opentextbooks/subjects/applied?locale=es"},{"id":82,"name":"Statistics","parent_subject_id":7,"call_number":"QA273-280","visible_textbooks_count":30,"url":"https://open.umn.edu/opentextbooks/subjects/statistics?locale=es"}],"publishers":[{"id":716,"url":"https://crumplab.github.io/Books.html","year":null,"created_at":"2019-06-29T11:35:52.000-05:00","updated_at":"2020-08-30T15:31:17.000-05:00","name":"Crump Lab"}],"formats":[{"id":1254,"type":"Online","url":"https://crumplab.github.io/statistics/","price":{"cents":0,"currency_iso":"USD"},"isbn":null},{"id":1255,"type":"PDF","url":"https://crumplab.github.io/statistics/","price":{"cents":0,"currency_iso":"USD"},"isbn":null}],"rating":"3.5","textbook_reviews_count":1,"reviews":[{"id":4170,"first_name":"Esther","last_name":"Loanzon","position":"Faculty/Dept Chair","institution_name":"Portland Community College","comprehensiveness_rating":3,"comprehensiveness_review":"Gives down to earth and real life applications of statistical data. Though, glossary is missing , it would be helpful to have this for quick reference.","accuracy_rating":4,"accuracy_review":"Content is objective, mostly accurate but can still need some editing of texts.","relevance_rating":3,"relevance_review":"Examples of research studies are outdated (some as old as 1963). Empirical references are valued but information and times change rapidly. We are in a digital world where software programs are available to acquire faster , comprehensive and more accurate data analysis.","clarity_rating":4,"clarity_review":"Jargon are used effectively and in simple, comprehensible language for a beginning student to understand concepts easily.","consistency_rating":4,"consistency_review":"Content terminology and framework are coherent and structured in an orderly manner for quick understanding.","modularity_rating":3,"modularity_review":"Topics are divided into too many reading sections which can be helpful for quick reference but can also be distracting for some readers. A major topic heading and chunking subheading would be appropriate to realign concepts clearly. Different strategies of understanding concepts are presented such as concrete examples, short practice exercises/drills, video clips and graphics.","organization_rating":4,"organization_review":"Topics are presented in a systematic approach making it easy for readers to follow the sequence, anticipate the next topic and cross reference past topics.","interface_rating":4,"interface_review":"No major issues were encountered with video clips. Graphics for charts and tables can still be improved using digital application to enhance reader/student enthusiasm.","grammatical_rating":4,"grammatical_review":"Too few and minor to notice.","cultural_rating":3,"cultural_review":"As pointed out earlier, even if some of the references used were dated (1965 until 1975), there are no overt cultural sensitive issues presented. Broad examples were given that may not be sensitive or inclusive of race, ethnicity and other background.","overall_rating":7,"overall_review":"The book is a helpful manual for beginning psychology students or any student taking up psychology as an elective course or minor program. It would have been more effective if a glossary was provided for quick reference to terminology or jargon.","created_at":"2020-06-30T11:24:52.000-05:00","updated_at":"2020-06-30T11:24:52.000-05:00"}],"url":"https://open.umn.edu/opentextbooks/textbooks/answering-questions-with-data-introductory-statistics-for-psychology-students?locale=es","updated_at":"2026-05-11T02:07:33.000-05:00"},{"id":559,"title":"Learning Statistics with R: A tutorial for psychology students and other beginners","edition_statement":null,"volume":null,"copyright_year":2018,"ISBN10":null,"ISBN13":null,"license":"Attribution-ShareAlike","language":"eng","accessibility_statement":"","accessibility_features":"","description":"Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. The book discusses how to get started in R as well as giving an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book.","contributors":[{"id":4546,"contribution":"Author","primary":true,"corporate":false,"title":"Dr","first_name":"Danielle","middle_name":null,"last_name":"Navarro","location":"University of New South Wales","background_text":"Danielle Navarro, PhD is a computational cognitive scientist at the University of New South Wales. Her research focuses on human concept learning, reasoning and decision making. She is also interested in language and cultural evolution, cognitive development, and statistical methods in the behavioural sciences"}],"subjects":[{"id":35,"name":"Applied","parent_subject_id":7,"call_number":"QA37.3","visible_textbooks_count":48,"url":"https://open.umn.edu/opentextbooks/subjects/applied?locale=es"},{"id":7,"name":"Mathematics","parent_subject_id":null,"call_number":"QA1","visible_textbooks_count":177,"url":"https://open.umn.edu/opentextbooks/subjects/mathematics?locale=es"},{"id":82,"name":"Statistics","parent_subject_id":7,"call_number":"QA273-280","visible_textbooks_count":30,"url":"https://open.umn.edu/opentextbooks/subjects/statistics?locale=es"},{"id":42,"name":"Psychology","parent_subject_id":9,"call_number":"BF121","visible_textbooks_count":51,"url":"https://open.umn.edu/opentextbooks/subjects/psychology?locale=es"},{"id":9,"name":"Social Sciences","parent_subject_id":null,"call_number":"H1","visible_textbooks_count":285,"url":"https://open.umn.edu/opentextbooks/subjects/social-sciences?locale=es"}],"publishers":[{"id":527,"url":"http://compcogscisydney.org/learning-statistics-with-r/","year":null,"created_at":"2018-09-07T12:22:40.000-05:00","updated_at":"2020-01-02T23:48:49.000-06:00","name":"Danielle Navarro"}],"formats":[{"id":891,"type":"PDF","url":"https://learningstatisticswithr.com/lsr-0.6.pdf","price":{"cents":0,"currency_iso":"USD"},"isbn":null},{"id":1925,"type":"LaTeX","url":"https://github.com/djnavarro/rbook/tree/master/original/tex","price":{"cents":0,"currency_iso":"USD"},"isbn":null}],"rating":"4.5","textbook_reviews_count":4,"reviews":[{"id":3493,"first_name":"Jessica","last_name":"Salvatore","position":"Associate Professor","institution_name":"Sweet Briar College","comprehensiveness_rating":5,"comprehensiveness_review":"This text, version 0.6, clocks in at over 600 manuscript pages (to date no version has been typeset) -- but the length is worth it to gain great coverage. Navarro covers not only everything you could expect to learn in a two-course sequence of undergraduate behavioral science statistics -- descriptive statistics, probability, analysis of variance,  regression, and a very welcome chapter on Bayesian approaches-- plus how to implement a lot of data description and analysis in R, including step-by.  It is a effective and useful mashup of these two topics.\r\n\r\nIt does not have an index or glossary.  I did not miss them, as it’s easy to use the search function to find the first instance of any term within the text, and Navarro is very good about defining and contextualizing new terms clearly as she goes.\r\n","accuracy_rating":5,"accuracy_review":"Version 0.6 appeared free of errors as far as I could see.  Furthermore, it did a nonpartisan job of framing debates that are throwing out a lot of light at the moment (such as the debates between proponents of frequentist vs. Bayesian approaches).  Navarro’s approach is exemplary: she carefully contextualizes the issues at stake, explains why she feels as she does, and provides useful resources to follow up in more detail.","relevance_rating":5,"relevance_review":"The content of the book seems up-to-date; indeed, at the moment I write this, the book has emerged as a common recommendation on social media for those hoping to learn R, so it’s clear that it is broadly seen as relevant.  Navarro has already implemented several revisions, showing that necessary updates are easy and straightforward to incorporate.  The core information in the book (statistics) is nearly timeless and should not need constant updating.","clarity_rating":5,"clarity_review":"Clarity is absolutely paramount when one is attempting to learn a new skill -- or to learn two new skills, R and statistics, as is envisioned here.  Navarro is an extremely useful guide to this process: it’s as if she takes your hand and walks you through step by step, so that learning these new skills is quite painless.  Version 0.6 features clear and carefully chosen examples, no doubt honed over the prior versions.  As noted above, new terms are clearly defined (almost obviating the need for a glossary or index).","consistency_rating":5,"consistency_review":"Navarro has thought carefully about when/where and how to introduce new concepts, and then is thoughtful in using them consistently.  She includes summary sections at the end of each chapter that are more helpful than ‘typical’ summaries, and useful sample R code is provided where appropriate.","modularity_rating":4,"modularity_review":"Where possible, the book is modular.  For example, a reader who is reasonably competent in statistics but using the book to learn R would have no trouble using the Table of Contents and closing chapter summaries to jump right to a specific section (say on graphing) that captures what they need to learn.  A reader who is trying to use the book to learn statistics would be best advised to go in order, as later topics build on earlier ones and trying to do an end-run around this organization is ill-advised, but this is not a flaw of the text per se; it is inherent in learning about the topic.","organization_rating":5,"organization_review":"The book is organized carefully and intentionally. There is a ‘received organization’ to most texts that introduce behavioral statistics, building in terms of complexity (for example, covering t-tests before moving to analysis of variance).  The book follows this in the relevant sections (most of its second half). There was more scope for choice and design in the book’s first half, which introduces all of the topics the reader will need to understand before diving into implementing statistical learning in R.  Here, Navarro has done a fantastic job of making choices that are friendly to the reader.","interface_rating":4,"interface_review":"The text is rendered as a PDF, and everything is laid out quite cleanly, with helpful clickable links in the Table of Contents to each section.","grammatical_rating":5,"grammatical_review":"The text was very competently copy-edited (despite being still being in, as it were, beta-testing) and did not appear to contain any unintentional errors.  \r\n\r\n","cultural_rating":5,"cultural_review":"I found nothing culturally insensitive or offensive in the text.  The author is not based in North America, which was occasionally lightly apparent, which I consider all to the good.  \r\n","overall_rating":10,"overall_review":"A valuable asset of this book is its congenial tone.  Navarro is chatty and funny, sometime even a bit irreverent, and the reader benefits quite a bit from this well calibrated conversational tone.\r\n","created_at":"2020-01-10T22:46:28.000-06:00","updated_at":"2020-01-10T22:46:28.000-06:00"},{"id":4834,"first_name":"Pete","last_name":"Martini","position":"Assistant Professor","institution_name":"Manchester University","comprehensiveness_rating":4,"comprehensiveness_review":"The book did a very good job of gently working students up to analyses in R. The text was clear and incorporated existing datasets that students (and faculty) could use to engage in hands-on learning.","accuracy_rating":4,"accuracy_review":"Because of the plethora of R packages, there will always be some discussion about which packages are \"easier\" to use or more appropriate for particular sections. However, this text does a good job of guiding students toward the construction of an R package toolbox that is appropriate for social science analysis.","relevance_rating":5,"relevance_review":"The only significant changes that are likely to be needed in the book are alterations to, or selection of, different packages if and when they arise. Those changes should be relatively easy to make.","clarity_rating":5,"clarity_review":"This text is very clear. There is some jargon with R that one must become accustomed to, but once students understand the jargon (which is really essential to understanding how the R environment works) the text is clear and easy to understand.","consistency_rating":5,"consistency_review":"No real comments here. The text is as consistent as one would expect from a book teaching students statistics in R.","modularity_rating":4,"modularity_review":"The text has clear section delineation. As is true of many \"beginner\" texts teaching a particular statistical platform, the units largely build off one another. So, although the sections are very well delineated, I would not recommend rearranging the chapters as that would likely not benefit students.","organization_rating":5,"organization_review":"The structure of the book made good sense and made R feel more accessible.","interface_rating":4,"interface_review":"My one comment would be a link (at the end of sections or chapters) to take the reader back to the Table of Contents.","grammatical_rating":5,"grammatical_review":"No significant errors.","cultural_rating":5,"cultural_review":"I did not see any cultural insensitivities in my review of the book.","overall_rating":9,"overall_review":null,"created_at":"2021-04-23T11:48:24.000-05:00","updated_at":"2021-04-23T11:48:24.000-05:00"},{"id":4888,"first_name":"Zheng","last_name":"Zhou","position":"Ph.D. Candidate","institution_name":"Indiana University - Bloomington","comprehensiveness_rating":5,"comprehensiveness_review":"This books covers the fundamentals in both statistics and R programming.\r\nI would suggest add a little touch of Bayesian statistics in the section of the Stats Theory given the broad application of Bayesian inference in psychology.","accuracy_rating":5,"accuracy_review":"Content is accurate, error-free and unbiased in my opinion.","relevance_rating":5,"relevance_review":"Contents are up-to-date.","clarity_rating":5,"clarity_review":"Delivery of statistical theories in this book is clear, with the support of reproducible examples and relevant practice questions.","consistency_rating":5,"consistency_review":"Terminology is consistent throughout the book.","modularity_rating":5,"modularity_review":"Each chapter is divided into accessible modules that can be assigned to the course segments by demand.","organization_rating":5,"organization_review":"It would be better if the author could add a brief \"landmark\" in the beginning to help the readers decide:\r\nWhich chapters I need to read If I want to learn X given Y time? e.g. Which chapters to read If I want to learn about chi square test so that I can i) work on a dataset and interprets the results on next week's presentation or ii) develop in-depth understanding of the analysis and use it in my thesis in half a year.","interface_rating":5,"interface_review":"Interface is clear and compact, my favorite style!","grammatical_rating":5,"grammatical_review":"The text contains no grammatical errors to the best of my knowledge.","cultural_rating":5,"cultural_review":"I feel the text is not culturally insensitive or offensive in any way.","overall_rating":10,"overall_review":null,"created_at":"2021-05-05T14:23:02.000-05:00","updated_at":"2021-05-05T14:23:02.000-05:00"},{"id":33521,"first_name":"Raphael","last_name":"Mondesir","position":"Associate Professor","institution_name":"Seattle Pacific University","comprehensiveness_rating":5,"comprehensiveness_review":"This text (Learning Statistics with R ~  Ed. 0.6) covers every major topic one would expect to encounter in an introductory statistics course, and then some. It will teach its readers everything from levels of measurement, random variables, and probability distributions to p-values, ANOVA, Chi-Square, and multiple regression. In fact, one could argue that it’s a little ambitious in its coverage. For example, do instructors really teach “Bayesian analysis” in an introductory course? \r\nMany concepts in statistics (and the way they’re typically taught) are the subject of fierce debates among statisticians. Instead of merely giving students formulas and distribution density plots, Dr. Navarro takes the more daring approach of not avoiding the controversies, but instead decides to inform the students as much as possible on why certain camps across the disciplines have their preferences. The author often traces the origin of concepts and their most common applications in a way that can only enhance student learning. This book probably contains the most comprehensive, critical, and unbiased discussion I have seen on the most controversial issues in the contemporary practice of statistics: sampling, p-values, degrees of freedom, hypothesis testing, Student T-test, linear regression, etc. \r\nThe author gives thorough explanations that move the reader from the most elementary facts to the richest explorations of scientific questions. There is an even a little blurb about power analysis – but the author acknowledges that it would need to be more deeply explored in an expanded section or chapter. In any case, I am happy to see that this author is a lot more forthcoming about the real usefulness of power analysis and they did not encourage undergrads to blindly join the power analysis craze that statisticians like Andrew Gelman have warned us about. Here’s a book that covers more than most manuals at this level of instruction usually do and still finds the humility to openly state what it could have done better. Bravo! \r\nOverall, this is a truly lively book with good examples and a solid R package to support coding and learning R/RStudio.","accuracy_rating":5,"accuracy_review":"As far as I know, the book contains no conceptual error or technically misleading statements. Its discussion of the concepts is extensive, balanced and impartial.","relevance_rating":4,"relevance_review":"The text is written in a way that should keep it relevant for years to come. However, I am on the fence about the introductory chapters that walk students through the psychology of statistics. I wonder about the effectiveness of such material – maybe some will get it, most will probably not. My advice? Save it for a more advanced course. \r\nAlso, this textbook’s emphasis is more on offering simple coding tricks rather than on students learning to work out the calculations with pen and paper. Yes. I prefer the latter. Or at least I use a combination of both. In my view, the speed and convenience that coding provides is only for students who have earned their stripes by doing the procedures with pen and paper first. I firmly believe that’s students learn better if they can perform the paired t-test by hand before executing it in their R script. \r\nHaving an R package `lsr` with pre-loaded functions and data to accompany the text is great for student. lsr as a package also incorporates clever solutions and tricks that overcome and extend some of the limitations of the simple functions in base R for describing data and investigating relationships between two or more variables. However, should there be a time in the future when this lsr package is no longer maintained and updated then students who will continue to refer to it or new cohorts of students may be in trouble. The standard student t-test tools in R/RStudio are not likely to go away, but insular packages get obsolete, developers neglect them, etc. There is a potential hazard of learning the basics of statistics using this lsr package instead of the standard R tools... This nice package has its potential downside too, and the possibility that it might not be maintained in the future is one of them.","clarity_rating":4,"clarity_review":"The text is written in lucid prose, using a conversational tone that is likely to draw both students and instructors in. It is clear that Dr. Navarro cares deeply about the reader. However, the explanations are not always as clear as they could be. For example, the explanation for the p-value does not land as well as I hoped. Next to the p-value, I find the concept of degrees of freedom the hardest thing to explain to a student unfamiliar with statistical reasoning. This text attempts to be as comprehensive as possible with both concepts. It does a decent job introducing them but uses too many words and, in the end, students may still be unclear about the p-value is or what degrees of freedom really are. In both cases the explanation involves too much backtracking, delaying, and the explanation is then spread over several sections. By the time the explanation fully unwraps, it is a little too convoluted to sink in. To be fair, I cannot do a better job; I was simply hoping this otherwise great (free) textbook would do better than what I have seen in every other text (which is to simply mention “df” like it’s a natural fact and use it in a formula without any prefatory remark).","consistency_rating":5,"consistency_review":"The mathematical shorthand and terminology in this text may not necessarily be what one finds in the vast majority of textbooks, but it is simpler and\r\n consistent throughout. More importantly, the text is highly consistent in its approach to how statistics should be taught: by offering students full coverage on how concepts and methods are typically used and why there is disagreement over their usage. The author takes great care to underline the pros and cons of competing methods – for example, see its masterful exposition of the two main methods for doing the One-sample T-test. It’s a joy to read!\r\nAnother example of the text remaining consistent is in the approach to hypothesis testing. For instance, significance levels are not chosen in advance… Rather, students perform the tests and then figure out at what level of significance their result may be relevant. I find it more realistic with how we determine the significance of results in regression models for scientific publication.","modularity_rating":3,"modularity_review":"The PDF version is this free text is searchable and very easy to navigate. Some, though not all, of the chapters can be used as standalone units. Unfortunately, many references like “as we saw earlier” or “back in chapter Y, we did ...”,  or worse \"similarly to how we performed a chi-square test in chapter N...\" negatively impact the book’s modularity. While these references may make the book more readable as a whole, they certainly get in the way of the adaptability of the sections and subsections as self-defined units.\r\nAn instructor can teach some of the topics in a different order than how the author presents them, but it will require a little effort in planning. I, for one, cannot assign the chapters in the order they’re presented. But these self-referential disruptions are not necessarily a dealbreaker. This is still an impressive textbook.","organization_rating":4,"organization_review":"From the polished table of contents to the numbered chapters and sections, to the neat graphs and the data tables used in the elaborate examples – it is clear that the text is well-organized and a lot of thought went into the sequence of the units. The summary section at the end of each chapter is golden. It’s the little things that help students remember what’s worth remembering.  \r\nHowever, there are some features that could make the material more accessible. The most glaring omissions are the lack of practice sets and homework problems. Granted, the examples in the text are very elaborate and easy to follow, but the text sorely lacks some exercises and homework problems with solutions that could really help students solidify their knowledge before moving on to the next unit/chapter. I would have preferred a guided lab and some problems with solutions over the chapter on Bayesian analysis that almost no one is going to teach in an introductory course. The book also lacks, statistical tables (Z, T, F distributions etc.) – usually that helps students do the hypothesis tests quicker. I want students to learn doing the T-test, ANOVA, and Pearson’s correlation test by hand. That way they get to peek behind the curtain of what R does. Printed statistical tables is one set of tools that facilitate that process. I understand why this book excludes this material: the focus is on doing these procedures in R/RStudio/ That’s fine, but I wish it encouraged students to practice doing these procedures with a pen and paper first. \r\nStill, this is a FREE book, and perhaps we are (really, I am) asking too much of it.","interface_rating":4,"interface_review":"The text’s presentation is nearly flawless. it’s a highly readable book in PDF format with full search functionality. My only gripe does not concern the book itself, but rather the supplemental materials associated with the lsr package to which students are introduced in this text. More specifically, the book doesn’t always tell students how to get the data they will need in order to follow along with an example. Yes, the data sets are posted online and can be downloaded, but… The data sets are NOT pre-loaded with the lsr library on CRAN. Why not? That would make everything so much simpler! In my experience, I find most undergraduates to be notoriously bad at downloading and opening data sets in R/RStudio. Perhaps other instructors have had better luck, but I have wasted far too many workshop hours helping students figure out where their lost files are hiding in the nooks and crannies of their expensive laptops (unfortunately, you can’t do much statistical analysis on a phone, the device on which they're an expert). I have lost that battle, and it’s ok. They will learn how to use their computer in another class beyond mine. For now, I just want them to be able to complete the coding exercises without me facing an avalanche of emails that all begin the same way: “HELP: I can’t find my data”.  \r\nTo Dr. Navarro, my most urgent plea is this one: please include the data sets used in the text with the next version of the lsr library so we can simply load them in RStudio. Then, I will adopt this textbook and not look back. Not for a long time...","grammatical_rating":4,"grammatical_review":"It is hardly the case that a text of this size contains no typos or grammatical errors. For a free text, however, it surprisingly contains very few errors.","cultural_rating":5,"cultural_review":"This text presents concepts in a way that is culturally neutral for the most part. I simply wish that the word “Psychology” was not splashed on the cover – why potentially limit your audience? I know several social scientists who are to skip this text simply because of the title -- they might assume it is better tailored to the needs of psychology students. Utter rubbish: a good intro to statistics book is hard to find across the board, so I find the title and the attempt at intra-disciplinary location a self-imposed limitation that is hardly justified. Giving advice on how to report the results of a hypothesis test, the author writes: “the best advice I can give is to suggest that you look at papers/reports written in your field and see what the convention seems to be” -- this clearly assumes that the book is aimed at readers from a variety of disciplines. Thus, the term “psychology” slapped of the cover is misleading. While some statistical techniques may be more familiar to members of one academic discipline versus another, I don’t see what is to be gained by falsely signaling to students that there is a “statistical science” for psychology, a different one for economics and another for sociology, etc...","overall_rating":9,"overall_review":"Let’s be honest: most introductory statistics textbooks are just not that interesting. As an instructor, I typically dread adopting one almost as much as my students dread reading it. Dr. Navarro has given us a book (literally) that may change that. It is rare that a paid textbook is this good, let alone one that is entirely free. I won’t argue that this book is perfect – it is not, but it is an impressive contribution for which we should be highly grateful. There are a few choices that I found questionable, but most of the content is presented fairly and judiciously. Here is a text that both instructors and students might actually enjoy reading. \r\nThis book is especially well suited for instructors who teach a sequence of two or three courses in statistics. Because there is so much content, it easily lends itself to continued learning from introductory level to intermediate courses (with some supplemental materials, of course). It’s perhaps the best book I have encountered on the subject – and it’s free! And it uses R.  Enough said.","created_at":"2021-12-10T19:27:37.000-06:00","updated_at":"2021-12-10T19:27:37.000-06:00"}],"url":"https://open.umn.edu/opentextbooks/textbooks/learning-statistics-with-r-a-tutorial-for-psychology-students-and-other-beginners?locale=es","updated_at":"2026-05-11T02:07:59.000-05:00"}],"links":{"self":"https://open.umn.edu/opentextbooks/subjects/statistics.json?locale=es?page=1","total_pages":4,"total_count":33,"next":"https://open.umn.edu/opentextbooks/subjects/statistics.json?locale=es?page=2"}}