type 1 vs type 2 error

Type 1 error, or false positive, is the incorrect rejection of a true null hypothesis in statistical hypothesis testing. Type 2 error, or false negative, is the incorrect failure to reject a false null hypothesis. These errors represent two types of mistakes that can occur when making statistical decisions.

Context and Usage

Type 1 and type 2 errors are fundamental concepts in statistical hypothesis testing, used by researchers, data scientists, and analysts across scientific research, medical testing, quality control, and A/B testing. They appear whenever decisions are made about population parameters based on sample data, particularly in fields requiring evidence-based conclusions such as medicine, psychology, and product development.

Common Challenges

Users often confuse the two error types or struggle with the trade-off between them, as reducing one typically increases the other. Misinterpretation can occur when p-values are misunderstood or when statistical significance is confused with practical importance. The technical language of hypothesis testing can create barriers to understanding, leading to misapplication in real-world scenarios.

Related Topics: null hypothesis, statistical significance, p-value, power analysis, alpha level, beta level, false positive, false negative