The p-value represents the probability of concluding (incorrectly) that there is a difference in your samples when no true difference exists. It is a statistic calculated by comparing the distribution of given sample data and an expected distribution (normal, F, t, etc.) and is dependent upon the statistical test being performed. For example, if two samples are being compared in a t-test, a p-value of 0.05 means that there is only 5% chance of arriving at the calculated t value if the samples were not different (from the same population). In other words, a p-value of 0.05 means there is only a 5% chance that you would be wrong in concluding the populations are different. P-value < 0.05 = safe to conclude there’s a difference. P-value = risk of wasting time investigating further.
