Interpretation of p-value: The Correct Way!

Abstract

The probability value (p-value) is used in hypothesis testing to assist in determining if the null hypothesis should be rejected. In a practical setting, the p-value helps to determine if an experiment is conducted and then compares the outcomes to what random chance may yield. In order to do it, researchers state a “null hypothesis” that they want to disapprove. Many researchers consider the p-value to be the essential summary of statistical analysis of their research data. Although it is undeniable that p-values are a very useful method for summarizing study results, it is also undeniable that p-values are frequently misused and misunderstood. Therefore p-value must be carefully interpreted based on the study design, sample size, comparability of study groups, and appropriateness of statistical tests. The statistically significant p-value should not be the sole criterion for accepting or rejecting the conclusions of any report or publication. Proper critical appreciation of research publications is a mandatory requirement before making clinical decisions based on them