gen-briefly-explain-type-i-and-type-ii-errors-and-one-common-mis-c65f

Briefly explain type I and type II errors and one common misconception about it.

# Type I and Type II Errors **Type I Error (False Positive):** Rejecting a true null hypothesis—concluding there's an effect when there actually isn't one. The probability of this error is α (alpha), typically set at 0.05. *Example:* Concluding a drug works when it's actually ineffective. **Type II Error (False Negative):** Failing to reject a false null hypothesis—missing a real effect that exists. The probability of this error is β (beta). *Example:* Concluding a drug doesn't work when it actually does. ## Common Misconception **The p-value is NOT the probability of making a Type I error.** Many believe that p = 0.03 means "3% chance the result is a false positive," but the p-value actually represents the probability of observing data this extreme *if the null hypothesis were true*. The actual probability of a Type I error (given you reject H₀) depends on the prior probability that H₀ is true, which the p-value doesn't account for. This confusion has led to widespread misinterpretation of statistical significance in research.