He clearly meant "fail to reject" where he said "accept", you're quibbling about semantics. The whole post is about how p-values (and indeed, known statistical techniques) don't actually help you decide whether or not you should reject the null.
Re:Why I've lost faith in p values
by thaumasiotes on Tuesday April 24, 2018 @07:35PM
(#16917596)
The post is about a confusion between the question that p-values address ("how likely is this data to have come from the null hypothesis?") and another very different question ("given several findings that were unlikely to have come from various different hypotheses, how many are likely to be spurious?").
>"That isn't a question that any statistical approach will help you with. There's a reason we talk in terms of "rejecting" or "failing to reject" a hypothesis. We don't do statistical tests to accept hypotheses, only to reject them."
In (Neyman-Pearson's) hypothesis testing you definitely can accept an hypothesis. This is not true for (Fisher's) significance testing though.
This paper is probably fine to explain it: https://onlinelibrary.wiley.com/doi/pdf/10.1002/0470013192.b...
Re:Why I've lost faith in p values
by throw16916145 on Wednesday April 25, 2018 @02:37AM
(#16919028)
The only comment to the point so far has gotten downvoted. How telling... Bashing p-values without really understanding what they are about is quite trending these days.
Re:Why I've lost faith in p values
by throwaway1982x on Tuesday April 24, 2018 @07:26PM
(#16917546)
I've never understood this semantic point of statistics.
When used in English, the word "accept" literally means "fail to reject", and has no more implication than that. Do people making this point use a different definition of "accept"?
A particularly glaring issue is this offhand comment:
> this is a statement about what happens when the null hypothesis is actually true. In real research, we don't know whether the null hypothesis is actually true. If we knew that, we wouldn't need any statistics! In real research, we have a p value, and we want to know whether we should accept or reject the null hypothesis.
That isn't a question that any statistical approach will help you with. There's a reason we talk in terms of "rejecting" or "failing to reject" a hypothesis. We don't do statistical tests to accept hypotheses, only to reject them.
The concept of accepting one hypothesis based on a comparison between it and one other hypothesis is ludicrous on its face, suffering exactly the problems associated with Pascal's wager.