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ANOVA is a hypothesis test to check whether the average differences between groups are significant or only due to random chance. It works when the Y variable is continuous and the X variable is discrete.

Practitioner’s Tip: 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.

Assumptions:

• Equal variances and normal distribution within the groups under comparison

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 an 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<α decide for the Alternative Hypothesis (there’s a difference)