On 2014 Feb 28, Jessie Tenenbaum commented:

This is a great paper for people like me who think about p-values, and yet are by no means statisticians- highly recommended.

I've been trying wrap my head about the fact that p-value does NOT mean the likelihood my hypothesis is correct. Here's what I've come up with:

I could hypothesize that a given male subject has only Y-containing sperm. We could then do the experiment of having him mate 5 times. If all 5 progeny come out as male, the p-value is under .05. That is, there is less than 5% chance those results could be observed by random chance. BUT that does NOT mean there is less than 5% chance that I am wrong, because it was a "long shot" (to use the article's phrase) to begin with.

Does that seem right? Any other examples that would better illustrate this point?

On 2014 Feb 28, Jessie Tenenbaum commented:

This is a great paper for people like me who think about p-values, and yet are by no means statisticians- highly recommended.

I've been trying wrap my head about the fact that p-value does NOT mean the likelihood my hypothesis is correct. Here's what I've come up with:

I could hypothesize that a given male subject has only Y-containing sperm. We could then do the experiment of having him mate 5 times. If all 5 progeny come out as male, the p-value is under .05. That is, there is less than 5% chance those results could be observed by random chance. BUT that does NOT mean there is less than 5% chance that I am wrong, because it was a "long shot" (to use the article's phrase) to begin with.

Does that seem right? Any other examples that would better illustrate this point?