In hypothesis testing, the p value is in routine use as a tool to make statistical decisions. It gathers evidence to reject null hypothesis. Although it is supposed to reject the null hypothesis when it is false and fail to reject the null hypothesis when it is true but there is a potential to err by incorrectly rejecting the true null hypothesis and wrongly not rejecting the null hypothesis even when it is false. These are named as type I and type II errors respectively. The type I error (α error) is chosen arbitrarily by the researcher before the start of the experiment which serves as an arbitrary cutoff to bifurcate the entire quantitative data into two qualitative groups as ‘significant’ and ‘insignificant’. This is known as level of significance (α level). Type II error (β error) is also predetermined so that the statistical test should have enough statistical power ((1–β)) to detect the statistically significant difference. In order to achieve adequate statistical power, the minimum sample size required for the study is determined. This approach is potentially flawed for the precision crisis due to choosing of arbitrary cutoff as level of significance and due to dependence of statistical power for detecting the difference on sample size. Moreover, p value does not tell about the magnitude of the difference at all. Therefore, one must be aware of these errors and their role in making statistical decisions.