A p-value (probability value) is a value used in statistical hypothesis testing (especially for research methodology) that is intended to determine whether the obtained results are significant or not.

In statistical hypothesis testing, the null hypothesis is a type of hypothesis that states a default position, such as there being no association among groups, or relationship between two observations. You can either accept it or reject it.

Assuming that the given null hypothesis is accepted, determining a p-value will help you figure out how likely it is that the observed results actually differ from the null hypothesis. It helps establish correlation-regression between the variables.

The smaller the p-value, the higher is its significance, and the more evidence there is that the null hypothesis should be rejected for an alternative hypothesis.

Typically, a p-value of ≤ 0.05 is accepted as significant and the null hypothesis is rejected, while a p-value > 0.05 indicates that there is not enough evidence against the null hypothesis to reject it.

Given that the data being studied follows a normal distribution, a Z-score table can be used to determine p-values, as in this calculator.