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cloud.google.com·7h ago
Data Science
Towards Data Science·Sep 11·5 min read
The 95% Illusion: Why Your Confidence Interval Isn't What You Think It Is
TL;DR
Frequentist confidence intervals and Bayesian credible intervals serve different purposes, and misinterpreting them can lead to flawed product decisions. Understanding these differences is crucial for accurate data analysis.
✦ Why It Matters
Ensure your data analysis aligns with the correct statistical framework to avoid costly product missteps.
Key Takeaways
- Differentiate between confidence intervals and credible intervals in reports.
- Use frequentist methods for objective analysis and reproducibility.
- Incorporate Bayesian reasoning to account for prior knowledge.
- Evaluate economic significance alongside statistical significance in large samples.
How It Works
A 95% confidence interval is constructed to ensure that, over many samples, 95% of such intervals will contain the true parameter. This is based on the Central Limit Theorem, which states that sample means will approximate a normal distribution as sample size increases.