🟪 1-Minute Summary
Hypothesis testing is a systematic way to determine if observed data provides enough evidence to reject a claim (null hypothesis). The universal framework: (1) State hypotheses (H₀ and H₁), (2) Choose significance level (α), (3) Calculate test statistic, (4) Find p-value or critical value, (5) Make decision (reject or fail to reject H₀), (6) Interpret in context. This same structure applies to all tests.
🟦 Core Notes (Must-Know)
The 6-Step Framework
Step 1: State the Hypotheses
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Step 2: Choose Significance Level (α)
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Step 3: Calculate the Test Statistic
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Step 4: Determine the p-value or Critical Value
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Step 5: Make a Decision
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Step 6: Interpret in Context
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Key Concepts
- Null Hypothesis (H₀): [To be filled in]
- Alternative Hypothesis (H₁): [To be filled in]
- p-value: [To be filled in]
- Significance level (α): [To be filled in]
- Type I Error: [To be filled in]
- Type II Error: [To be filled in]
🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“Explain the p-value in simple terms”
- [Answer: Probability of getting our result (or more extreme) if H₀ is true]
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“What’s the difference between Type I and Type II errors?”
- [Answer framework to be filled in]
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“Why do we use 0.05 as the significance level?”
- [Answer framework to be filled in]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Saying “accept the null hypothesis”
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Mistake 2: Confusing p-value with probability H₀ is true
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Mistake 3: Choosing α after seeing results
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🟩 Mini Example (Quick Application)
Scenario
[Simple A/B testing example to be filled in]
Solution
from scipy import stats
# Example to be filled in
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