<Since its invention 90 years ago, the p value has become the standard by which most quantitative research is judged; however, it was never intended for this purpose.1 Indeed, a 2016 joint statement by the American Statistical Association argued, “By itself, a p value does not provide a good measure of evidence regarding a model or hypothesis.”2 A lone p value is uninformative because it is prone to false positives and says nothing about the magnitude or range of an effect.3 Additionally, over-reliance on p values may even encourage unethical research practices.4>
<Given the p value’s limitations, it should not be the sole arbiter of publication, and researchers should always report additional information, especially means, standard deviations, confidence intervals, R2, and effect sizes. These additional statistics do not correct p values’ shortcomings – some of which were not mentioned here, but they make results sections more informative and help to hold researchers accountable.>
Interesting read on the pitfalls and misuses of P-Value. (Short Article). They also do a good job of defining it.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5738950/

             ***********************************************
The BEE-L mailing list is powered by L-Soft's renowned
LISTSERV(R) list management software.  For more information, go to:
http://www.lsoft.com/LISTSERV-powered.html