In 2016, the American Statistical Association released a statement in The American Statistician warning against the misuse of statistical significance and P values.
The false belief that crossing the threshold of statistical significance is enough to show that a result is ‘real’ has led scientists and journal editors to privilege such results, thereby distorting the literature.
On top of this, the rigid focus on statistical significance encourages researchers to choose data and methods that yield statistical significance for some desired result, or that yield statistical non-significance for an undesired result, such as potential side effects of drugs – thereby invalidating conclusions.
As in the anti-inflammatory-drugs example, interval estimates can perpetuate the problems of statistical significance when the dichotomization they impose is treated as a scientific standard.
The objection we hear most against retiring statistical significance is that it is needed to make yes-or-no decisions.
Our call to retire statistical significance and to use confidence intervals as compatibility intervals is not a panacea.
P values, intervals and other statistical measures all have their place, but it’s time for statistical significance to go.
This article was summarized automatically with AI / Article-Σ ™/ BuildR BOT™.