– ANOVA is a hypothesis test to check whether the average differences between groups are significant or only due to random chance
– The name ANOVA (ANalysis Of VAriances) comes from the statistical procedure the tool conducts. However, for the practitioner it’s important to remember that the tool compares averages not variances
– ANOVA requires equal variances and normal distribution within the groups under comparison
-It works when the Y variable is continuous and the X variable is discrete

Step-by-step approach:
1.Visualize the data: Use a stratified frequency plot, e.g. dot plot
2. Formulate hypotheses: H0: 𝜇1=𝜇2=𝜇3=𝜇4=𝜇5…
                                         HA: At least one m is different
3. Decide on Alpha risk: Usually 0.05
4. Select and conduct appropriate test: Retrieve p-value
5. Verify test assumptions
   – Data in the groups are normally distributed.
   – Groups don’t have different variances
6. Make a decision: If p<a decide for the Alternative Hypothesis (there’s a difference)