Understanding p-value in Statistics
The p-value helps to determine the significance of the results and is the probability in which we are allowing the results of the data to be wrong. The p-value will be a number between zero and one.
Another way to think of the p-value is as the confidence level that the researcher is correct with the data presented. P<0.05 means the results of the data can be wrong in 5 cases out of 100, 4 out of 100, 3 out of, 2 out of, 1 out of or 0 out of 100 cases. This is significant and gives the researcher confidence that the data in the study is accurate at least 95% of the time (5% uncertainty, or 95% certainty the sample data reflects the whole population).
If p<0.001, this means the researcher is willing to have the data disproven in 1 case out of 1,000 (0.1% uncertainty, or 99.9% certainty), even more significant than the probability factor of p<0.05.