You can abuse Bayesian methods just as easily you can hack a p-value. Maybe more easily, since fewer people would be aware of the issues.
What's needed is a shift in researcher attitudes and incentives, to emphasize development of reliable knowledge instead of publication record. Just changing the rules of the game slightly will only lead to people adjusting their game slightly.
Yes but with traditional frequentist approaches most scientists don’t understand what’s going on under the hood of the statistical tools they’re using. There are dozens and dozens of named tests like “Fisher’s exact test” and “Wilcoxon signed-rank test” and most scientists just sort of follow received wisdom about which test to use in which situation.
Bayesian methods force you to actually think about how your data and model parameters are distributed, and explicitly specify a model.
Bayesian methods force you to actually think about how your data and model parameters are distributed, and explicitly specify a model.
Yep. I know some very senior scientists who get a lot of mileage out of finding places where "model-free" methods are implicitly assuming a bloody stupid model, and then attacking them.
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You can abuse Bayesian methods just as easily you can hack a p-value. Maybe more easily, since fewer people would be aware of the issues.
What's needed is a shift in researcher attitudes and incentives, to emphasize development of reliable knowledge instead of publication record. Just changing the rules of the game slightly will only lead to people adjusting their game slightly.
http://www.stat.columbia.edu/~gelman/research/unpublished/p_...
Yes but with traditional frequentist approaches most scientists don’t understand what’s going on under the hood of the statistical tools they’re using. There are dozens and dozens of named tests like “Fisher’s exact test” and “Wilcoxon signed-rank test” and most scientists just sort of follow received wisdom about which test to use in which situation.
Bayesian methods force you to actually think about how your data and model parameters are distributed, and explicitly specify a model.
Yep. I know some very senior scientists who get a lot of mileage out of finding places where "model-free" methods are implicitly assuming a bloody stupid model, and then attacking them.