Big effect sizes tend to be fabricated. It's really disturbing. I do lots of biostatistics in academia and professors openly acknowledge many trials cherrypick

Big effect sizes tend to be fabricated. It's really disturbing. I do lots of biostatistics in academia and professors openly acknowledge many trials cherrypick data. The outcome is that if you are honest it's hard to get funded because your results look bad. Then, if we talk about honest results only, crappy statistics also leads to crazy optimistic effect estimates. Regularizing priors and pooling should be the norm. Andrew Gelman posts a lot about this: https://statmodeling.stat.columbia.edu/ https://statmodeling.stat.columbia.edu/