🟪 1-Minute Summary
Chi-square tests determine if there’s a significant association between categorical variables. Common types: (1) Test of Independence (are two variables related?), (2) Goodness of Fit (does data match expected distribution?). Use when both variables are categorical. The test compares observed frequencies to expected frequencies.
🟦 Core Notes (Must-Know)
What is a Chi-Square Test?
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Chi-Square Test of Independence
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Chi-Square Goodness of Fit
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Formula and Calculation
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Assumptions
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“When would you use a chi-square test vs a t-test?”
- [Answer: Chi-square for categorical, t-test for numerical]
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“How do you interpret a chi-square test result?”
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🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Using chi-square with small expected frequencies
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Mistake 2: Confusing independence with goodness of fit
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🟩 Mini Example (Quick Application)
Scenario
[Example: Gender vs Product Preference]
Solution
from scipy.stats import chi2_contingency
import pandas as pd
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