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