🟪 1-Minute Summary
ANOVA (Analysis of Variance) tests if means of 3+ groups are significantly different. Instead of multiple t-tests (which inflates Type I error), ANOVA does one omnibus test by comparing variance between groups to variance within groups. If significant, use post-hoc tests to find which specific groups differ. One-way ANOVA has one factor; two-way has two factors.
🟦 Core Notes (Must-Know)
What is ANOVA?
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One-Way ANOVA
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Two-Way ANOVA
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How ANOVA Works
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F-statistic in ANOVA
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Post-Hoc Tests
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Assumptions
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“Why use ANOVA instead of multiple t-tests?”
- [Answer: Multiple t-tests inflate Type I error (α)]
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“ANOVA is significant. What do you do next?”
- [Answer: Post-hoc tests like Tukey’s HSD to find which groups differ]
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“What does the F-statistic represent in ANOVA?”
- [Answer: Ratio of between-group variance to within-group variance]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Using ANOVA for 2 groups
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Mistake 2: Not checking assumptions
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Mistake 3: Stopping after significant ANOVA
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🟩 Mini Example (Quick Application)
Scenario
[Comparing website conversion rates across 4 page designs]
Solution
from scipy import stats
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🔗 Related Topics
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