Bayesian analysis overcomes this particular problem, but of course doesn't have such an easy and commonly accepted "THIS IS SIGNIFICANT AND BY GOLLY I AM RIGHT!" signal. It's conceptually harder than NHST, doesn't lead to easy conclusions (unless your effect is really strong), and many researchers have never even heard about it. And they are the reviewers.
I got into a bit of a kerfuffle about a statistical test once. A critical review insisted I do an ANOVA, even though it was inadequate, and I had used a weaker test. Such is the status quo.
Work has been done to make Bayesian analysis as straight-forward as NHST, but if it gets accepted with a threshold (comparable to the current p<0.05), it won't help.
Some articles now publish both analyses, which is nice. And there are tools to help you, e.g. https://jasp-stats.org/
I got into a bit of a kerfuffle about a statistical test once. A critical review insisted I do an ANOVA, even though it was inadequate, and I had used a weaker test. Such is the status quo.
Work has been done to make Bayesian analysis as straight-forward as NHST, but if it gets accepted with a threshold (comparable to the current p<0.05), it won't help.
Some articles now publish both analyses, which is nice. And there are tools to help you, e.g. https://jasp-stats.org/