Type I & Type II Errors

Definition

Type I error: rejecting a true null hypothesis (false positive, controlled by α). Type II error: failing to reject a false null (false negative, controlled by statistical power).

Where it's used

Justifying the 0.05 significance level in your methodology
Explaining why multiple comparisons need correction

Related concepts

Null Hypothesis p-value Statistical Power ANOVA Average Variance Extracted (AVE)Bootstrapping

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Where it's used

Related concepts

Guides that use this concept

Sampling Methods in Research: Types, Selection & Justification

How to Choose the Right Statistical Test: A Decision Guide

Qualitative vs Quantitative vs Mixed Methods: Which Fits Your Study?

Reliability and Validity Explained (With Examples)

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