[samch93]

The ASA recently published a new statement which is more optimistic about the use of p-values [1]. I myself also think that correctly used p-values are in many situations a good tool for making sense out of data. Of course, a decision should never be conducted on a p-value alone, but the same could also be said about confidence/credible intervals, Bayes factors, relative belief ratios, and any other inferential tool available (and I‘m saying this as someone who is doing research in Bayesian hypothesis testing methodology). Data analysts always need to use common sense and put the data at hand into broader context.

[1] https://projecteuclid.org/journals/annals-of-applied-statist...

[throwawaysea]

Is the nuance here that the ASA is OK with p-values but not OK with the rhetorical phrasings around statistical significance? My take is that it is easy to casually misinterpret or misrepresent statistical results because of how fuzzy these language around it all is. Phrases like "statistically significant" imply a certain kind of causality to the reader, when the actual rigorous claims are very specific and nuanced. Moving away from such soft phrasings might mean people have to stick to precise and narrow claims, whereas the normalization of soft phrasings makes room for bad claims or bad interpretations.