In statistical inferential analysis, p value (probability value) has a significant role as it is the probabilistic statement of test statistic on which evident conclusions are drawn. The p-value always lies between zero and one (both inclusive). As an assessment tool of statistical significance, p values are unavoidable in research activities since it helps in interpreting the results. The cut-off between a large and a small p-value is typically set at 0.05(5%), termed the critical level of significance. An arbitrary value of 0.05 is chosen to separate the probable from improbable or the significant from the insignificant. This is known as the level of significance (α) which is the size of likelihood acceptable that an observed difference is due to chance (usually α=0.05). If α is fixed at 0.05, p≤0.05 is considered statistically significant. This level will be defined or set by the researcher before the start of the study and acceptance/rejection of null hypothesis will be drawn based on the observed p value; if it is above the fixed level of significance or below. Variability in the reporting styles of p value has been observed in different journals and this is a matter of debate.