Stats for students (and everybody else)
A selection of resources about statistics, with a slight emphasis on R.
Five great papers
- Cumming, G., Fidler, F. & Vaux, D. L. Error bars in experimental biology. J Cell Biol 177, 7–11 (2007).
- Weissgerber, T. L., Milic, N. M., Winham, S. J. & Garovic, V. D. Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13, e1002128–10 (2015).
- Colquhoun, D. An investigation of the false discovery rate and the misinterpretation of p-values. Royal Society Open Science 1, 140216–140216 (2014).
- Lakens, D., Adolfi, F. G., Albers, C. J., Anvari, F., Apps, M. A. J., Argamon, S. E., … Zwaan, R. A. Justify your alpha. Nature Human Behaviour, 2(3), 168 (2018).
- Menge, D. N. L., MacPherson, A. C., Bytnerowicz, T. A., Quebbeman, A. W., Schwartz, N. B., Taylor, B. N., & Wolf, A. A. (2018). Logarithmic scales in ecological data presentation may cause misinterpretation. Nature Ecology & Evolution, 2(9), 1393–1402 about the paper
Four great (text)books on statistics and (sometimes) R
Modern Dive: An Introduction to Statistical and Data Sciences via R by Chester Ismay and Albert Y. Kim (free)
Modern Statistics for Modern Biology by Susan Holmes and Wolfgang Huber (free)
Learning Statistics with R by Danielle Navarro (free)
The Art of Statistics: Learning from Data by David Spiegelhalter
Journals’ resources on statistics
Josh Starmer’s YouTube channel
StatQuest with Josh Starmer “breaks down complicated Statistics and Machine Learning methods into small, bite-sized pieces that are easy to understand”.
Daniel Lakens & Jeff Leek (et al.)
Daniel Lakens runs probably the best introductory course on statistics on the internet: Improving your statistical inferences (keep an eye on his blog at The 20% Statistician as well.
Jeff Leeks (co-)runs multiple courses online, but if I had to pick one, it would be the Chromebook Data Science: “a free, massive open online educational program (…) to help anyone who can read, write, and use a computer to move into data science (…)”. As the name suggests, the only things you need to participate is an internet connection and a web browser.
Teacups, giraffes, & statistics - yes, I know, but just click there (also, examples use R in-browser).
The Permutation Test: A Visual Explanation of Statistical Testing by Jared Wilber (also with animals…)
Interpreting Cohen’s d effect size an interactive visualization (don’t miss the visualisations on correlations and power)
P values and and the null hypothesis significance testing: the good, the bad and the ugly
Scientists rise up against statistical significance: “Valentin Amrhein, Sander Greenland, Blake McShane and more than 800 signatories call for an end to hyped claims and the dismissal of possibly crucial effects.”
Objections to Frequentism… are exagerrated :-)