Not in general, but in this post, where he writes,
I’d like to flip it around and say: If we see something statistically significant (in a non-preregistered study), we can’t say much, because garden of forking paths. But if a comparison is not statistically significant, we’ve learned that the noise is too large to distinguish any signal, and that can be important.
Pointer from Mark Thoma. My thoughts:
1. Just as an aside, economists are sometimes (often?0 guilty of treating absence of evidence as evidence of absence. For example, if you fail to reject the efficient markets hypothesis, can you treat that as evidence in favor of the EMH? Many have. Similarly, when Bob Hall could not reject the random walk model of consumer spending, he said that this was evidence in favor of rational expectations and consumption smoothing.
2. I think that a simpler way to make Gelman’s point would be to say that passing a statistical significance test is a necessary but not a sufficient condition for declaring the evidence to be persuasive. In particular, one must also address the “selection bias” problem, which is that results that pass significance tests are more likely to be written up and published than results that fail to do so.