Type I and Type II Errors in Hypothesis Testing
Type I and Type II errors define the two ways a hypothesis test can go wrong — critical knowledge for AP Statistics, biostatistics, and research methods courses. A Type I error (false positive) rejects a true null hypothesis; a Type II error (false negative) fails to reject a false one. Understanding both shapes how researchers set significance levels and sample sizes.
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Type I vs Type II error in medical testing
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What is the difference between Type I and Type II errors?
A Type I error occurs when you reject a true null hypothesis (false positive), controlled by α. A Type II error occurs when you fail to reject a false null hypothesis (false negative), controlled by β.
- Type I: seeing an effect that is not there
- Type II: missing a real effect
How do you reduce Type II errors?
Reduce Type II errors by increasing statistical power: use a larger sample size, increase α (if acceptable), choose a more sensitive measurement, or focus on larger expected effect sizes. Power analysis before data collection helps determine the minimum n needed.
Why is α typically set at 0.05?
The 0.05 threshold is a convention established by Ronald Fisher — representing a 1-in-20 chance of a false positive. Fields with higher stakes (medicine, particle physics) use stricter thresholds like 0.01 or 0.0000003.