@richarddmorey @dstephenlindsay This is one where we had one hostile and 2 positive reviewers - may be useful to see the discussion - paper eventually accepted https://t.co/EktmhSZej2
@dnunan79 @enemiesnet Hmm, not quite. I'm looking for a really easy explainer, preferably with epidemiological examples. (And I wrote a paper with @stat_PT where we took issue with some of the conclusions from Head et al! https://t.co/EktmhSZej2.)
@bmwiernik @BoulesteixLaure sure here are some showing up so many issues I can't even count that far (there are quite some more but I don't have a good collection):
https://t.co/uaPKQbBMOG
https://t.co/KOp3YeR3wm
https://t.co/hQSAjEm3RN
https://t.co/gs6hzWZTQT
https://t.co/IKtUnXujff
#124 food for thought from @deevybee & @stat_PT on the misleading effects of ghost and correlated variables on p-curve analyses, and on the limits of text-mined p-values https://t.co/aVNSViBO2g @peanutbuttner you might find this interesting https://t.co/b4dtODpa4J
@wgervais @hardsci I just know from the simulations we did on p-hacking that things get more complex if you have correlated variables. https://t.co/EktmhSZej2
@EJWagenmakers @siminevazire @farid_anvari @mo279_mo I don’t think that’s right if there is really no effect: see our simulations - but you may not get ‘diagnostic ‘ L skew: https://t.co/EktmhSZej2. but big problem in automated analysis if no check on which pvalues included