A quote from the Brian himself illustrating how he is able to "publish" so much junk research.
> P-hacking shouldn’t be confused with deep data dives – with figuring out why our results don’t look as perfect as we want. With field studies, hypotheses usually don’t “come out” on the first data run. But instead of dropping the study, a person contributes more to science by figuring out when the hypo worked and when it didn’t. This is Plan B. Perhaps your hypo worked during lunches but not dinners, or with small groups but not large groups. You don’t change your hypothesis, but you figure out where it worked and where it didn’t.
This is horrifically bad research practice and basically guarantees that all of your results will be completely p-hacked.
Really? Suppose you just ran a twenty year, many million dollar field study, the hypothesis was not confirmed, you just throw everything out & that's that?
You’re being kind — He’s a former Cornell Prof who was removed from his position for making up data to fit PR-able conclusions. None of his work should be cited as evidence of anything.
lalaland1125|6 years ago
> P-hacking shouldn’t be confused with deep data dives – with figuring out why our results don’t look as perfect as we want. With field studies, hypotheses usually don’t “come out” on the first data run. But instead of dropping the study, a person contributes more to science by figuring out when the hypo worked and when it didn’t. This is Plan B. Perhaps your hypo worked during lunches but not dinners, or with small groups but not large groups. You don’t change your hypothesis, but you figure out where it worked and where it didn’t.
This is horrifically bad research practice and basically guarantees that all of your results will be completely p-hacked.
https://statmodeling.stat.columbia.edu/2018/09/23/tweeking-b... is another hilarious example.
ip26|6 years ago
dokein|6 years ago