Hypothesis Methods: Testing Multiple Groups with ANOVA

Inferential Statistics: Sample vs. Population

Hypothesis testing is based on the study of inferential statistics. It means that we take samples for an experiment and from those experiments we make generalizations about the population. We take samples because it would be either too complex or too costly to gather the population. A good example of this is drug testing.

A/B Testing and the Z-test

One of the simplest types of tests is A/B Testing. In general terms it is comparing A vs. B. To use our previous example, A would be the experimental drug and B would be a control group. Let’s assume the goal of the drug is to lower cholesterol. When compared to the control group, do the patients in the drug test sample have lower cholesterol?


ANOVA is for when you want to test multiple groups. Groups, aka levels, are subsections of an independent variable. As mentioned before with our medication example, what if it wasn’t just the one drug vs the control group and it was comparing three different drugs to see which was best. If we were to conduct multiple t-tests we would run into the multiple comparisons problem. ANOVA does not run into this problem because instead of comparing the averages, we are analyzing the variances between the different groups.

Variance (Wikipedia Image)



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