[rademacher]

Isn't there utility in accepting the null hypothesis? It's almost as valuable to know that there is no signal in the data as there is in the opposite, i.e., knowing where not to look for information.

I think your example is really justifying a "machine learner" that has some domain expertise and doesn't blindly apply algorithms to some array of numbers.

[whatshisface]

I think his argument is that some null hypotheses can be rejected out of hand, but that people are wasting time and effort obtaining evidence that, if they had better priors, would be multiplied by 0.0000000000001 to end up with an insignificant posterior. That's what the astrology example indicates.

[cpb]

The effort to evaluate the null hypothesis can be costly. In the competitive environment found in most hedge funds, how would you allocate to accepting the null hypothesis?

As in, if you worked at a data acquisition desk, and spent a quarter churning through terabytes of null hypothesis data, what's your attribution to the fund's performance?

[losteric]

I think they're describing the "look-elsewhere effect": https://en.wikipedia.org/wiki/Look-elsewhere_effect (aka https://en.wikipedia.org/wiki/Multiple_comparisons_problem)

[inputcoffee]

Accepting the null hypothesis has utility only if you have some reason to believe it would not be accepted.

Accepting it per se has no particular value. You could generate several random datasets, and accept/reject the null hypothesis between them ad infinitum.

To put it another way, its only interesting if its surprising.