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For your ANOVA results to be valid, your data generally needs to meet these criteria:

To find the specific culprit, you must run a (like Tukey’s HSD or Bonferroni). These tests go back in and safely compare the groups to identify which one stands out. For your ANOVA results to be valid, your

If you have four groups, you’d need to run six separate t-tests to compare them all. Every time you run a test, there's a 5% chance of a "false positive" (Type I error). By the time you finish those six tests, your chance of making an error has skyrocketed. ANOVA solves this by doing it all in one "omnibus" test, keeping your error rate in check. How ANOVA Works (The Simple Version) ANOVA looks at two types of variation: Every time you run a test, there's a

Depending on your data structure, you’ll likely use one of these three common types: 1. One-Way ANOVA How ANOVA Works (The Simple Version) ANOVA looks

Developed by the legendary statistician Ronald Fisher, the core "trick" of ANOVA is in its name: it analyzes (how spread out the data is) to make a judgment about the means (the averages). Why not just use multiple t-tests?

The "spread" (variance) should be roughly equal across all groups.