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Type I and Type II Errors in Statistics: Power Analysis and Sample Planning

Type I and Type II Errors in Statistics: Power Analysis and Sample Planning

The Two Errors Every Researcher Must Understand

Every statistical test can produce one of two types of errors. Understanding these errors is fundamental to sound research design — and thesis committees will test your knowledge of them.

Type I Error (α — False Positive)

Type I error occurs when you conclude that an effect exists when it does not. You reject a true null hypothesis. The probability of this error is controlled by your alpha level (α), conventionally set at 0.05. This means you accept a 5% chance of a false positive in any single test.

Type II Error (β — False Negative)

Type II error occurs when you fail to detect an effect that is truly present. You fail to reject a false null hypothesis. Beta (β) is typically set at 0.20, meaning researchers accept a 20% probability of missing a real effect. This is most common in underpowered studies.

Statistical Power (1 − β)

Power is the probability of correctly detecting a real effect. The standard is 1−β=0.80 (80% power). Power is influenced by:

Multiple Comparisons and Type I Error Inflation

Running many tests on the same dataset inflates the family-wise Type I error rate. For example, running 20 separate t-tests at α=.05 produces approximately one false positive by chance. Corrections such as Bonferroni (α/k) or Benjamini-Hochberg control this inflation.

G*Power for A Priori Power Analysis

Plan your sample size before data collection using G*Power. Enter your expected effect size, α level, and desired power to obtain the minimum required N. Report this calculation in your methods section to demonstrate rigorous planning.

APA Reporting Example

A priori power analysis was conducted using G*Power 3.1 (Faul et al., 2007). For a one-way ANOVA with three groups, assuming a medium effect size (f=.25), α=.05, and 80% power, a minimum total sample of 159 participants was determined.

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