Five great papers

  1. Cumming, G., Fidler, F. & Vaux, D. L. Error bars in experimental biology. J Cell Biol 177, 7–11 (2007).
  2. 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).
  3. Colquhoun, D. An investigation of the false discovery rate and the misinterpretation of p-values. Royal Society Open Science 1, 140216–140216 (2014).
  4. 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).
  5. 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

Journals’ resources on statistics

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.

Great visualisations

P values and and the null hypothesis significance testing: the good, the bad and the ugly

Teaching statistics

Bayesian statistics

(General) Linear Models

Various stuff