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 pvalues. 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

Nature’s Statistics for Biologists series (in particular their Practical guides).
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

Seeing Theory  a Visual Introduction to Probability and Statistics

Teacups, giraffes, & statistics  yes, I know, but just click there (also, examples use R inbrowser).

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 :)

Statement on pvalues from the American Statistical Association