Medical research literature until recently, exhibited substantial dominance of the Fisher′s significance test approach of statistical inference concentrating more on probability of type I error over Neyman-Pearson′s hypothesis test considering both probability of type I and II error. Fisher′s approach dichotomises results into significant or not significant results with a P value. The Neyman-Pearson′s approach talks of acceptance or rejection of null hypothesis. Based on the same theory these two approaches deal with same objective and conclude in their own way. The advancement in computing techniques and availability of statistical software have resulted in increasing application of power calculations in medical research and thereby reporting the result of significance tests in the light of power of the test also. Significance test approach, when it incorporates power analysis contains the essence of hypothesis test approach. It may be safely argued that rising application of power analysis in medical research may have initiated a shift from Fisher′s significance test to Neyman-Pearson′s hypothesis test procedure.