the conundrum of pvalues: statistical significance is unavoidable but need medical significance too.

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.