We have seen the p-value before. A higher p-value of a test suggests that the sample results are consistent with the null hypothesis. Correspondingly, to reject the null hypothesis, you like to have lower p-values.
p-values are probabilities observing this extreme sample statistics when the null hypothesis is correct. For example, if 0.05 is the p-value of a study to test the effectiveness of a drug, then you should understand that even if the medicine has no effect, 5% of the studies will give the results you obtained.
It doesn’t stop here. People now think that 5% is the error rate of the test. And this is termed the p-value fallacy. The error associated with a particular p-value is estimated to be much higher than the p-value.
