Data Science

Standard Error (SE)

Arun Murali

Tags: data-science; statistics; inference; hypothesis-testing

🟪 1-Minute Summary

Standard Error (SE) measures the variability of a sample statistic (like the sample mean). Formula: SE = σ / √n. It gets smaller as sample size increases. SE is crucial for calculating confidence intervals and test statistics. Don’t confuse with standard deviation: SD describes data spread, SE describes estimate precision.

🟦 Core Notes (Must-Know)

What is Standard Error?

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Formula

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Standard Error vs Standard Deviation

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Why It Matters

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Relationship to Sample Size

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🟨 Interview Triggers (What Interviewers Actually Test)

Common Interview Questions

  1. “What’s the difference between standard deviation and standard error?”

    • [Answer: SD = data spread, SE = estimate precision]

  2. “What happens to SE as sample size increases?”

    • [Answer: Decreases (inversely proportional to √n)]

  3. “How is SE used in hypothesis testing?”

    • [Answer framework to be filled in]

🟥 Common Mistakes (Traps to Avoid)

Mistake 1: Using SD when you should use SE

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Mistake 2: Thinking larger SE is better

[Content to be filled in - smaller SE = more precise estimate]

🟩 Mini Example (Quick Application)

Scenario

[Sample mean calculation with SE]

Solution

import numpy as np

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🔗 Related Topics

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