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📚 Answering Questions with Data
Chapters ▾
0 Preface1 Why Statistics?2 Describing Data3 Correlation4 Probability, Sampling, and Estimation5 Foundations for inference6 t-tests7 ANOVA8 Repeated Measures ANOVA9 Factorial ANOVA10 More On Factorial Designs11 Simulating Data12 Thinking about answering questions with data13 GIFs14 References

Copying the textbook

This textbook was written in R-Studio, using R Markdown, and compiled into a web-book format using the bookdown package. In general, I thank the larger R community for all of the amazing tools they made, and for making those tools open, so that I could use them to make this thing.

All of the source code for compiling the book is available in the GitHub repository for this book:

https://github.com/CrumpLab/statistics

In principle, anybody could fork or otherwise download this repository. Load the Rproj file in R-studio, and then compile the entire book. Then, the individual.rmd files for each chapter could be edited for content and style to better suit your needs.

If you want to contribute to this version of the textbook, you could make pull requests on GitHub, or discuss issues and request on the issues tab.

Acknowledgments

Thanks to the librarians at Brooklyn College of CUNY, especially Miriam Deutch, and Emily Fairey, for their support throughout the process. Thanks to CUNY for supporting OER development, and for the grant we received to develop this work. Thanks to Jenn Richler for letting me talk about statistics all summer long.

Why we did this

Why write another statistics textbook, aren’t there already loads of those? Yes, there are. We had a couple reasons. First, we would like to make R more accessible for the undergraduate population, and we wrote this textbook around the capabilities of R. The textbook was written entirely in R-Studio, and most of the examples have associated R-code. R is not much of a focus in the textbook, but there is an introduction to using R to solve data-analysis problems in the lab manual. Many instructors still use SPSS, Excel, or newer free GUIs like JAMOVI, so we also made lab exercises for each of those as well.

This is a mildly opinionated, non-traditional introduction to statistics. It acknowledges some of the major ideas from traditional frequentist approaches, and some Bayesian approaches. Much of the conceptual foundation is rooted in simulations that can be conducted in R. We use some formulas, but mostly explain things without formulas. The textbook was written with math-phobia in mind, and attempts to reduce the phobia associated with arithmetic computations. There are many things missing that should probably be added. We will do our best to add necessary things as we update the textbook.

Adapted from Answering Questions with Data: Introductory Statistics for Psychology Students, by Matthew J. C. Crump (Brooklyn College of CUNY), crumplab.com/statistics, licensed under CC BY-SA 4.0. Portions adapt Danielle Navarro's Learning Statistics with R (CC BY-SA). Changes were made; this adaptation is distributed under the same license. License: CC-BY-SA-4.0.