A p-value (probability value) is a statistical measurement that tells you: How likely it is that your data could have occurred under the null hypothesis. The probability of obtaining the observed results, assuming that the null hypothesis is true. The level of statistical significance, expressed as a p-value between 0 and 1. It represents the probability of obtaining results as extreme as, or more extreme than, the observed results under the assumption that the null hypothesis is true. The common critical value that is set for statistical testing is p≤0.05(5%). This means that if the p-value obtained from your data is less than or equal to 0.05, then that means that the probability of you getting the results that you did or more extreme (assumming the null hypothesis is true) is so small, that it would be much more likely that the null hypothisis is false. If the p-value obtained is greater than 0.05, then that means that the probability of getting the data results that you have or more extreme are high enough where the null hypothesis could likely be true.
P-values are used in statistical hypothesis testing to determine the significance of results in relation to the null hypothesis. Here's why we use p-values: To help decide whether to reject the null hypothesis. To measure the significance of observational data. To quantify the statistical significance of a result.
To calculate the p-value, you need to know some important things about your data like the f-value and the f-critical value. That being said, while you can calculate the p-value by hand, it is faster and more efficient to let statistical software do it for you (Excel, DATAtab, etc.).