"In real life, we weigh the anticipated consequences of the decisions that we are about to make. That approach is much more rational than limiting the percentage of making the error of one kind in an artificial (null hypothesis) setting or using a measure of evidence for each model as the weight."
Longford (2005) http://www.stat.columbia.edu/~gelman/stuff_for_blog/longford.pdf
Surveys, coincidences, statistical significance 
"What Educated Citizens Should Know About Statistics and Probability"
By Jessica Utts, in 2003: https://ics.uci.edu/~jutts/AmerStat2003.pdf via @hrefna
There even wikipedia on the "Misuse of p-values": https://en.wikipedia.org/wiki/Misuse_of_p-values
I therefore am adding to my guidelines: "Instead of telling researchers what they want to know, statisticians should teach researchers which questions they can ask. […]
Before we can improve our statistical inferences, we need to improve our statistical questions."
Excerpt from Daniël Lakens (2021) https://journals.sagepub.com/doi/10.1177/1745691620958012
In 2016, the American Statistical Association #ASA made a formal statement that "a p-value, or statistical significance, does not measure the size of an effect or the importance of a result".
It also stated that "p-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone".
