The Conundrum of P-Values: Statistical Significance is Unavoidable but Need Medical Significance Too

Abstract

Background: Small P-values have been conventionally considered as evidence to reject a null hypothesis in empirical studies. However, there is widespread criticism of P-values now and the threshold we use for statistical significance is questioned.
Methods: This communication is on contrarian view and explains why P-value and its threshold are still useful for ruling out sampling fluctuation as a source of the findings.
Results: The problem is not with P-values themselves but it is with their misuse, abuse, and over-use, including the dominant role they have assumed in empirical results. False results may be mostly because of errors in design, invalid data, inadequate analysis, inappropriate interpretation, accumulation of Type-I error, and selective reporting, and not because of P-values per se.
Conclusion: A threshold of P-values such as 0.05 for statistical significance is helpful in making a binary inference for practical application of the result. However, a lower threshold can be suggested to reduce the chance of false results. Also, the emphasis should be on detecting a medically significant effect and not zero effect.