😯 I didn't know! whoopsie! 🙃

@dingdingpeng.the100.ci @lakens.bsky.social @eikofried.bsky.social ‬@aggieerin.bsky.social‬
‪@drcpennington.bsky.social‬ ‪@gpwilliams.bsky.social‬ ‬ ‪@willjharrison.bsky.social ‪@guidomaiello.bsky.social‬‪

I’ve surely missed someone — please share with anyone else interested. Thanks a lot!

This project has been great fun to work on. I’d love to hear from people involved in teaching statistics or research methods — your thoughts and feedback would be highly appreciated!

If you’d like to have a look, evaluation copies can be requested here: www.mheducation.co.uk/professional...

A special highlight for me was having an endorsement from Massimiliano Pastore, my stats prof during my studies, appear on the back cover. His support makes this book one of the most rewarding achievements of my career so far

Among other things, we tried to use recent open datasets from contemporary research to make examples feel fresh and relevant. We also cover slightly more advanced topics, including multilevel linear and logistic models, while keeping the material accessible to students.

And while we are both enthusiastic about Bayesian methods, in the book we stick to frequentist approaches. These remain the most widely used in the psychology literature students encounter.

But we point readers to excellent resources such as by @rmcelreath.bsky.social and @avehtari.bsky.social

We place strong emphasis on effect sizes & estimation, and confidence intervals — alongside p-values — so students appreciate the multiple way to make inferential decisions.

We also made some deliberate design choices. For example, although we mention the tidyverse, the book is mostly based on base R. We think it’s important that undergraduates also get exposed to core coding concepts (e.g. loops), which can otherwise be hidden by handy higher-level tidyverse functions.

There is lots of code and hands-on R examples. We emphasize simulations as key tool for power and sensitivity analyses, and discuss reproducible practices, taking inspiration from the principles of open science.

We also wanted to go beyond providing a set of applied "recipes". The book introduces key ideas from probability theory and causal inference to promote more solid foundations for research and statistical literacy. For example, we discuss probability spaces, DAGs, data-generating processes, and more.

This book was a labour of love. We aimed to provide psychology undergraduates a fresh approach to statistics, based on a unified framework where t-tests, ANOVA, regression, and multilevel models are all taught as special cases of the linear model.