The p-value tells the strength of evidence supporting the null hypothesis. It is the probability of a test statistics assuming null hypothesis is true. In other words, it is statistically value which provides details how much evidence is to reject the common explanation for the data set. It is the probability of obtaining extreme as one observed given that null hypothesis is true. If p-value is smaller than the level of significance, it means that the p-value is statistically significant because null hypothesis is rejected. It measures how well your sample data supports the null hypothesis to be true. The value is normally between 0 and 1 and interpreted as:
A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis. A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis. P-values very close to the cutoff (0.05) are considered to be marginal (could go either way). Always report the p-value so your readers can draw their own conclusions.
