Some Facts That You Might Be Unaware of About the P-Value

Recently, some articles on the P-value published in biomedical journals caught my eye. These articles included the words 'mis-use, ' 'misconception, ' or 'misinterpretation' of the P-value in their titles or abstracts. Nuzzo [1] stated that P-values, the 'gold stan-dard' of statistical validity, are not as reliable as many scientists assume and added that 'the P-value was never meant to be used the way it's used today. ' Greenland et al. [2] provided an explanatory list of 18 misinterpretations of P-values and guidelines for improving statistical interpretation. Other interesting articles include a paper by Chavalarias et al. [3] and the accompanying editorial by Kyriacou [4] published in the Journal of the Ameri-can Medical Association, and a review paper by van Rijn et al. [5]. Not only in biomedical communities, but also in a major statistical society, a statement on P-values has been issued in order to sound the alarm about the misuse of P-values [6]. Having been a statistician at medical schools for more than 10 years, I understand very well how obsessed many researchers are with P-values regarding the results of their studies. I have seen many times that some researchers try to obtain a P-value less than 0.05 from a P-value slightly greater than 0.05, such as 0.053 or 0.06, by deleting or adding some data. This happens, I think, because many researchers often believe that results with a P-value < 0.05, which is considered to be 'statistically significant, ' are truly scientifically or substantially significant. However, this is one of the most notorious misinterpretations of the P-value. With this in mind, how can we correctly interpret P-values? To answer this question, we should understand what a P-value really is. Informally, a P-value is the probability under a specified statistical model that a statistical summary of the data would be equal to or more extreme than its observed value [6]. I admit that this definition is not easy to understand. An important point is that it should not be used as a definitive decision-making tool, yielding outcomes of yes or no. A P-value is a probability. It is a measure of summarizing the incompatibility between a particular set of data and a proposed model for the data (the null hypothesis) [6]. Ronald Fisher, who introduced the P-value , intended it as an informal way to judge whether evidence was significant in the sense of being worthy of a …