A Type I error in statistics, also known as a false positive, occurs when a true null hypothesis is incorrectly rejected. In other words, it means concluding that there is a statistically significant effect or difference when, in reality, there is none-the observed result happened purely by chance or due to unrelated factors
. The probability of making a Type I error is denoted by the significance level alpha (α), commonly set at 0.05 (5%). This means there is a 5% chance of rejecting the null hypothesis when it is actually true. If the p-value from a statistical test is less than α, the result is considered statistically significant, but this carries the risk of a Type I error if the null hypothesis is indeed true
. Type I errors are errors of commission-erroneously concluding an effect exists. For example, in medical testing, a Type I error would be diagnosing a healthy patient as having a disease (a false positive). In hypothesis testing, it means declaring a treatment or change effective when it is not
. To reduce Type I errors, researchers can lower the significance level (e.g., from 0.05 to 0.01), apply corrections for multiple comparisons, design studies carefully, and avoid premature conclusions from incomplete data
. In summary:
- Type I error = rejecting a true null hypothesis (false positive)
- Probability of Type I error = significance level α (commonly 0.05)
- Consequences include false claims of effects or differences
- Control methods include setting stricter α levels and careful experiment design
