Significance Tests: t-Test, F-Statistic, ANOVA and More -- with Python

This phenomenon is more prevalent in research results where the decision is solely based on the observed data. Observed data alone is not useful and reliable unless the sampling procedure is carefully designed, and strict precaution is taken to avoid sampling biases which might lurk into the data and makes result biased. You can find more details on the statistical biases here. In order to derive a scientific conclusion based on the data, we should equip ourselves to significance testing, a.k.a. Hypothesis testing is used to help you learn that the difference between two groups is not due to random chance.