Direct comparison
Type I and Type II Errors Explained
Type I error = false positive; Type II error = false negative. Compare definitions, symbols (α, β), causes, and how researchers reduce each in study design.
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How do Type I Error (α), Type II Error (β) compare side by side?
The table below compares Type I Error (α), Type II Error (β) across 10 procurement-relevant dimensions, from also called through higher-stakes example.
Side-by-side comparison
| Dimension | Type I Error (α) | Type II Error (β) |
|---|---|---|
| Also called | False positive | False negative |
| What happens | Rejects a true null hypothesis | Fails to reject a false null hypothesis |
| Conclusion drawn | An effect exists when it actually doesn't | No evidence of an effect when one actually exists |
| Probability symbol | α (alpha) | β (beta) |
| Set/controlled by | Researcher, before the study (significance level, conventionally 0.05) | Sample size, effect size, and measurement precision, planned via a power analysis |
| Complement | 1−α = confidence level | 1−β = statistical power |
| Typical cause | Multiple comparisons, p-hacking, optional stopping | Small sample size, small true effect, noisy measurement |
| Origin | Neyman-Pearson framework, late 1920s–early 1930s | Neyman-Pearson framework, late 1920s–early 1930s |
| Reduced by | Lowering α, correcting for multiple comparisons, pre-registration | Larger sample size, more reliable measurement, a priori power analysis |
| Higher-stakes example | Confirmatory drug-approval trials weight this more heavily | Diagnostic screening tests often weight this more heavily (missed disease) |
Common questions
Common questions about Type I Error (α) vs Type II Error (β)
Is a Type I error the same as a false positive?
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Yes — in hypothesis testing the two terms are used interchangeably: the test signals an effect exists when the null hypothesis is actually true.
What is the relationship between Type II error and statistical power?
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Power equals 1−β. If the Type II error rate (β) is 0.20, power is 80% — they describe the same test from opposite directions.
Does a larger sample size fix both errors?
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It reduces Type II error (raises power) directly. It doesn’t change α, which the researcher sets as the significance threshold — but a larger sample makes it easier to hold α fixed while still reaching adequate power.
Why is 0.05 the conventional significance level?
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It is a widely used historical convention, not a statistical law. Fields with higher false-positive stakes or heavy multiple-testing burdens (e.g., genomics, particle physics) commonly use much stricter thresholds.
