🟪 1-Minute Summary
The F-test compares variances between two groups to determine if they’re significantly different. It’s the ratio of two variances: F = variance₁ / variance₂. Commonly used to test assumptions before t-tests (equal variance assumption) and as the foundation for ANOVA. F-distribution is right-skewed and always positive.
🟦 Core Notes (Must-Know)
What is an F-test?
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F-statistic Formula
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F-distribution
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When to Use F-test
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Assumptions
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“Before running a t-test, what should you check about the variances?”
- [Answer: Check if variances are equal using F-test or Levene’s test]
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“What does a large F-statistic indicate?”
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🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Forgetting F-test is sensitive to normality
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Mistake 2: Confusing F-test with ANOVA
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🟩 Mini Example (Quick Application)
Scenario
[Comparing variability in two manufacturing processes]
Solution
from scipy import stats
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