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by tst
4399 days ago
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I would even go further. The biggest problem isn't just using p values and R^2. I think the biggest problem is that a lot of people didn't learn statistics properly. Properly is a vague term. So what do I mean? Instead of obsessing with tons of techniques going back to the basic and actually learn how to do design studies, work with data, learn statistical reasoning and critical thinking. I took quite a few courses in statistics because I liked it. But a lot other people – especially those that apply statistics – maybe take one or two courses in stats and then do research / studies. The results can be pretty terrible. In conclusion, more fundamentals and less icing on the cake. |
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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.