A Type II error occurs when a statistical test fails to reject a false null hypothesis, leading to a false negative result. It is inversely related to the power of a test, meaning that as the probability of a Type II error decreases, the test's ability to detect an effect when there is one increases.

Type II Error

Type II error

A statistical test is a method used to determine if there is a significant effect or relationship present in a dataset, often by comparing observed data against a null hypothesis. The choice of test depends on the data type, distribution, and the specific research question being addressed.

statistical test

false null hypothesis

A false negative result occurs when a test fails to detect a condition or attribute that is actually present, potentially leading to missed diagnoses or overlooked issues. This type of error is critical in fields like medicine and security, where the consequences of not identifying a true positive can be severe.

false negative result

The power of a test is the probability that it correctly rejects a false null hypothesis, reflecting the test's ability to detect an effect when there is one. A higher power indicates a lower risk of Type II error, making it a crucial consideration in experimental design and statistical analysis.

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