Type I and Type II Errors

Statistics · Axiom Academy

LESSON Type I and Type II Errors Understanding the two fundamental types of errors in hypothesis testing and their tradeoffs 1. Type I Error (False Positive) A Type I error occurs when we reject the null hypothesis H₀ when it is actually true. This is also called a "false positive" because we incorrectly conclude there is an effect when none exists. The probability of making a Type I error is controlled by our chosen significance level α. If we set α = 0.05, we accept a 5% chance of rejecting a true null hypothesis. 2. Type II Error (False Negative) A Type II error occurs when we fail to reject the null hypothesis H₀ when it is actually false. This is also called a "false negative" because we miss detecting a real effect that exists. The probability of making a Type II error is β. The complement (1 - β) is called statistical power , which represents the probability of correctly rejecting a false null hypothesis. 3. Comparing Type I and Type II Errors Both errors are undesirable, but they represent different kinds of mistakes. Understanding the relationship between them is essential. These errors have an inverse relationship: decreasing α (being more conservative) typically increases β (reduces power). This creates a fundamental tradeoff in hypothesis testing. Understanding these errors becomes clearer when we consider practical applications where the consequences of each error type differ.

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