Thanks for this, looking forward to read it! I would also recommend in turn the paper by Greenland et al (2016) called "Statistical tests, P values, confidence

Thanks for this, looking forward to read it! I would also recommend in turn the paper by Greenland et al (2016) called "Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations.". It's a great read. I recently had a chat with a colleague who has "abandoned p-values for bayes factors" in their research, on how, in principle, there's nothing stopping you from having a "bayesian" p-value (i.e. the definition of the p-value at its most general can easily accommodate bayesian inference, priors, posteriors, etc). The counter-retort was more or less "no it can't, educate yourself, bayes factors are better" and didn't want to hear about it. It made me sad. p-values are an incredibly insightful device. But because most people (ab)use it in the same way most people abuse normality assumptions or ordinal scales as continuous, it's gotten a bad rep and means something entirely different to most now by default.