[wirrbel]

While I agree with the premise that most people do not know enough statistics, there are several underlying problems to that.

When I was first encountered to statistical topics as an undergrad, the lecturer himself was not confident of the matter and taught a very bare subset that consisted of linear fitting, error propagation, means and standard deviation. Even back then I had the feeling that the equations provided were insufficient and contained a lot of things that were not motivated or explained.

Nowadays I can see why I was not introduced to statistics and probabilities back then as I was introduced to algebra, analysis and quantum mechanics. The field of statistics is complex, full of contradicting best-practices and analytically challenging. Let alone Bayes-vs.-Frequentist, etc. In a way I doubt that all researchers working with Poisson-distributed data once in their life as scientists can work through the details Poissonian statistics analytically.

Maybe an illustrative introduction would be more beneficial. I imagine people could perform statistical experiments before working with real data and experience first hand how misleading a small data subset can be for example, or how fundamentally data plots can change their face. Maybe then people would stop being overenthusiastic about their N=20 experiments.