These stilted bear figures and their voices are sufficiently obnoxious in their own right, even without the tedious lampooning of p-values and the feigned horror at learning they should not be reported as posterior probabilities. Coincidentally, I have been sent several different p-value U-Tube clips in the past two weeks, rehearsing essentially the same interpretive issues, but this one (“what the p-value”*) was created by some freebee outfit that will apparently set their irritating cartoon bear voices to your very own dialogue (I don’t know the website or outfit.)
The presumption is that somehow there would be no questions or confusion of interpretation were the output in the form of a posterior probability. The problem of indicating the extent of discrepancies that are/are not warranted by a given p-value is genuine but easy enough to solve**. What I never understand is why it is presupposed that the most natural and unequivocal way to interpret and communicate evidence (in this case, leading to low p-values) is by means of a (posterior) probability assignment, when it seems clear that the more relevant question the testy-voiced (“just wait a tick”) bear would put to the know-it-all bear would be: how often would this method erroneously declare a genuine discrepancy? A corresponding “Bayesian bear” video practically writes itself, but I’ll let you watch this first. Share any narrative lines that come to mind.
*Reference: Blume, J. and J. F. Peipert (2003). “What your statistician never told you about P-values.” J Am Assoc Gynecol Laparosc 10(4): 439-444.
**See for example, Mayo & Spanos (2011) ERROR STATISTICS
