The most commonly used such method in medicine is a meta-analysis. To appropriately combine evidence from more than one study, we need to use a statistical method, NOT compare p-values. Wikipedia has a fantastic and very accessible article on the (), as also indicated above. The probability of one of these hypotheses being significant by random chance is 1 − (1 − 0.05) ^20 = 64% probability. Let's say you have 20 hypotheses that you want to test and are using a significance threshold of. Multiple hypothesis testing is a huge problem in the sciences. Fisher created the concept of a 'p value' and the 'null hypothesis' (no relationship between two measured phenomena) when he published about statistical significance tests in 1925, later starting in 1928 Neyman and Pearson began introducing the terminology for null and alternative hypothesis testing that are regularly used today. Well known scientists, such as Harvard's Miguel Hernan, have vouched to stop using the terminology 'statistically significant'. Unfortunately, this terminology has led to a misconception and many primarily look at the p-value, rather than the magnitude and direction of the effect being measured, to understand its significance. () a funny clip from FiveThirtyEight, where scientists actually working on research quality and p-values at Stanford (including one of the authors of this paper) are asked on the spot to explain p-values! This paper represents the first time that I know of, where they united forces into writing an article, reflecting the importance of addressing these misinterpretations.

These are heavyweight names in epidemiology/biostatistics. This was the first time ASA had ever produced such a statement of concern.

This same sentiment was expressed in the American Statistical Association's (ASA) (), to which this article served as a supplement.

Data dredging significado