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You noticed that the p-values in a paper are close to .05. Does this make you a bad person? Can 'circling p-values' ever be a useful heuristic? My two cents on an (apparently?) controversial topic.

Let's talk about errors in research, using myself as an example.

Creating studies that are powered to detect the smallest effect of interest... without collecting more data than you need to detect bigger effects

What happens when two researchers attempt to reproduce all the pre-registered studies with open data published in JCR?

If power analysis cannot be based on the expected effect size, what should it be based on?

Why power analysis, as traditionally performed, isn't a good tool for choosing sample size.

Can p-values be close to .05 because researchers ran careful power analysis, and collected 'just enough' participants to detect their effect? In this blog post, I evaluate the merits of this argument.

A short blog post showing the immense benefits of pre-registration on false-positive rates, even when the pre-registration is underspecified.

What can we learn from effect sizes, and under which conditions?

How can researchers respectfully and constructively flag inadequate statistical evidence when we they see it in papers? This post offers some personal reflections on this complex question.