OpenIntro Statistics

Reviewed by Emiliano Vega, Mathematics Instructor, Portland Community College on 12/5/16

Comprehensiveness rating: 5

For a Statistics I course at most community colleges and some four year universities, this text thoroughly covers all necessary topics. For example, types of data, data collection, probability, normal model, confidence intervals and inference for single proportions.

A thoughtful index is provided at the end of the text as well as a strong library of homework / practice questions at the end of each chapter.

Content Accuracy rating: 5

The content is accurate in terms of calculations and conclusions and draws on information from many sources, including the U.S. Census Bureau to introduce topics and for homework sets.

Errors are not found as of yet. The content stays unbiased by constantly reminding the reader to consider data, context and what one’s conclusions might mean rather than being partial to an outcome or conclusions based on one’s personal beliefs in that the conclusions sense that statistics texts give special. Some examples of this include the discussion of anecdotal evidence, bias in data collection, flaws in thinking using probability and practical significance vs statistical significance.

Relevance/Longevity rating: 4

The text is up to date and the content / data used is able to be modified or updated over time to help with the longevity of the text. For example, a scatterplot involving the poverty rate and federal spending per capita could be updated every year. Another example that would be easy to update and is unlikely to become non-relevant is email and amount of spam, used for numerous topics.

The probability section uses a data set on smallpox to discuss inoculation, another relevant topic whose topic set could be easily updated. This selection of topics and their respective data sets are layered throughout the book. The book uses relevant topics throughout that could be quickly updated.

Clarity rating: 4

The writing style and context to not treat students like Phd academics (too high of a reading level), nor does it treat them like children (too low of a reading level). The text meets students at a nice place medium where they are challenged with thoughtful, real situations to consider and how and why statistical methods might be useful.

For example, a goodness of fit test begins by having readers consider a situation of whether or not the ethnic representation of a jury is consistent with the ethnic representation of the area. The introduction of jargon is easy streamlined in after this example introduction.

Consistency rating: 5

Notation is consistent and easy to follow throughout the text. The text’s selection for notation with common elements such as p-hat, subscripts, compliments, standard error and standard deviation is very clear and consistent.

Tables and graphs are sensibly annotated and well organized. Distributions and definitions that are defined are consistently referenced throughout the text as well as they apply or hold in the situations used.

Modularity rating: 2

Each chapter consists of 5-10 sections. These sections generally are all under ten page in total.

This easily allow for small sets of reading on a class to class basis or larger sets of reading over a weekend.

Each section within a chapter build on the previous sections making it easy to align content.

For example, the inference for categorical data chapter is broken in five main section. Single proportion, two proportions, goodness of fit, test for independence and small sample hypothesis test for proportions. This keeps all inference for proportions close and concise helping the reader stay uninterrupted in the topic.

Organization/Structure/Flow rating: 4

The topics are presented in a logical order with each major topics given a thorough treatment. The text begins with data collection, followed by probability and distributions of a random variable and then finishing (for a Statistics I course) with inference.

Perhaps an even stronger structure would see all the types of content mentioned above applied to each type of data collection. That is, do probability and inference topics for a SRS, then do probability and inference for a stratified sample and each time taking your probability and inference ideas further so that they are constantly being built upon, from day one!

Interface rating: 4

Navigation as a PDF document is simple since all chapters and subsection within the table of contents are hyperlinked to the respective section. Graphs and tables are clean and clearly referenced, although they are not hyperlinked in the sections.

The only visual issues occurs in some graphs, such as on page 40-41, which have maps of the U.S. using color to show “intensity”. However with the print version, which can only show varying scales of white through black, it can be hard to compare “intensity”.

Grammatical Errors rating: 5

No grammatical errors have been found as of yet.

Cultural Relevance rating: 3

The text would not be found to be culturally insensitive in any way, as a large part of the investigations and questions are introspective of cultures and opinions. For example, income variations in two cities, ethnic distribution across the country, or synthesis of data from Africa.

The book has a great logical order, with concise thoughts and sections. While section are concise they are not limited in rigor or depth (as exemplified by a great section on the "power" of a hypothesis test) and numerous case studies to introduce topics.

The reading of the book will challenge students but at the same time not leave them behind.

Overall I like it a lot. The best statistics OER I have seen yet.