Curdin Derungs: Spatial Statistics or Why Spatial is Special?

Regina Nuzzo: How not to fool yourself with statistics: Cognitive biases & understanding and communicating p-values

Joaquín Ordieres Meré: Does data sampling matter at Big data scale?

David Colquhoun: P values provide poor evidence: some proposals for improving reproducibility

Haim Dubossarsky et al.: Language change outta control. Frequency bias in laws of semantic change.

R script from the course.

R script planned for the course (longer version).

.csv file.

RStudio Cheatsheets

Find here some of the references that were mentioned during the workshop or that might be interesting for the discussions we had:

Benjamin, D. et al. (2017): "Redefining statistical significance", Nature Human Behaviour.

• Response by Amrhein, V. & S. Greenland (2017): "Remove, rather than redefine, statistical significance", Nature Human Behaviour.
• Response by D. Lakens et al. (2017): "Justify your Alpha", PsyArXiv.

Colquhoun, David (2017a): "An investigation of the false discovery rate and the misinterpretation of p-values", Royal Society Open Science. 1: 140216.

Colquhoun, David (2017b): "The reproducibility of research and the misinterpretation of P values", BioRxiv.

Kilgariff, A. (2005): "Language is never, ever, ever random", Corpus Linguistics and Linguistic Theory 1-2, 263-276.

Nuzzo, R. (2014): "Statistical errors: P values, the “gold standard” of statistical validity, are not as reliable as many scientists assume", Nature 506(7487), 150–152.

Nuzzo, R. (2015): "How scientists fool themselves – and how they can stop", Nature 526(7572), 182–185.