This is out of Stanford University. The lead author is John Ioannidis. I was surprised the use of the p value has increased and not decreased. Hopefully, scientists such as Ioannidis will reverse this trend.

A review of p-values in the biomedical literature from 1990 to 2015 shows that these widely misunderstood statistics are being used increasingly, instead of better metrics of effect size or uncertainty

My bold.The widespread misuse of p-values — often creating the illusion of credible research — has become anembarrassmentto several academic fields, includingpsychologyand biomedicine, especially since Ioannidis began publishing critiques of the way modern research is conducted

“The p-value does not tell you whether something is true. If you get a p-value of 0.01, it doesn’t mean you have a 1 percent chance of something not being true,” Ioannidis added. “A p-value of 0.01 could mean the result is 20 percent likely to be true, 80 percent likely to be true or 0.1 percent likely to be true — all with the same p-value. The p-value alone doesn’t tell you how true your result is.”

For an actual estimate of how likely a result is to be true or false, said Ioannidis, researchers should instead use false-discovery rates or Bayes factor calculations

https://med.stanford.edu/news/all-n...values-showing-up-more-often-in-journals.html

My knowledge of Bayesian statistics is very minimal. I did find the following but I'm not sure how helpful it is. If anyone has a better source, feel free to post it. Thanks.

https://en.m.wikipedia.org/wiki/Bayesian_probability