# Statistical Thinking for the 21st Century

Russell A. Poldrack, Stanford University

Copyright Year: 2018

Publisher: Russell Poldrack

Language: English

## Formats Available

## Conditions of Use

Attribution-NonCommercial

CC BY-NC

## Reviews

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 at psychology students enrolled in a statistics course, there are no homework exercises, nor is their an... read more

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

at psychology students enrolled in a statistics course, there are no homework exercises, nor is their an index at this point in time.

There are numerous omissions, typographical errors, etc. in this draft. The book appears to have potential as a textbook once completed. The

author is using R as a platform for data visualization and modeling, which is an appropriate choice for the material being presented.

The draft of the textbook contains numerous errors and omissions.

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

a tibble rather than a standard data frame). The content is modular, which means that chapters can be easily updated.

In the chapters which are complete, the presentation is lucid.

Statistical methods tend to have an ad hoc nature, which means that it is difficult to have notation that remains consistent throughout.

Chapters are often paired, with an introductory chapter covering the statistical method followed by a chapter on the implementation in R,

which is helpful. The author does well in presenting statistical concepts without the benefit of a strong mathematical foundation.

The chapters are largely self-contained. It will be difficult for students who are new to the R language to generalize their understanding

of the functions to a more general setting from just an example or two.

The pairing of the chapters (for example, Chapter 12 is titled Sampling and Chapter 13 is titled Sampling in R) should be helpful

for the students in order to separate the statistical technique from its implementation.

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

close 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

the future.

The author is clearly a good writer. Once the draft gets completed, the typos removed, exercises written, and an index generated, this

book would be appropriate as a college-level statistics text for psychology students.

The author does not limit the discussion to just psychology applications. Examples ranging from 2017 election results to Steph Curry free throw results are included.

## Table of Contents

- 1 Introduction
- 2 Working with data
- 3 Probability
- 4 Summarizing data
- 5 Fitting models to data
- 6 Data Visualization
- 7 Sampling
- 8 Resampling and simulation
- 9 Hypothesis testing
- 10 Confidence intervals, effect sizes, and statistical power
- 11 Bayesian statistics
- 12 Modeling categorical relationships
- 13 Modeling continuous relationships
- 14 The General Linear Model
- 15 Comparing means
- 16 The process of statistical modeling: A practical example
- 17 Doing reproducible research

## Ancillary Material

## About the Book

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.

## About the Contributors

### Author

**Russell A. Poldrack**