## What is a 'Type II Error'

A type II error is a statistical term used within the context of hypothesis testing that describes the error that occurs when one accepts a null hypothesis that is actually false. The error rejects the alternative hypothesis, even though it does not occur due to chance. A type II error fails to reject, or accepts, the null hypothesis, although the alternative hypothesis is the true state of nature.

## BREAKING DOWN 'Type II Error'

A type II error confirms an idea that should have been rejected, claiming the two observances are the same, even though they are different. When conducting a hypothesis test, the probability, or risks, of making a type I error or type II error should be considered.## Differences Between Type I and Type II Errors

The difference between a type II error and a type I error is a type I error rejects the null hypothesis when it is true. The probability of committing a type I error is equal to the level of significance that was set for the hypothesis test. Therefore, if the level of significance is 0.05, there is a 5% chance a type I error may occur.

The probability of committing a type II error is equal to the power of the test, also known as beta. The power of the test could be increased by increasing the sample size, which decreases the risk of committing a type II error.

## Hypothesis Testing Example

Assume a biotechnology company wants to compare how effective two of its drugs are for treating diabetes. The null hypothesis states the two medications are equally effective. The alternative hypothesis states the two drugs are not equally effective.

The biotech company implements a large clinical trial of 3,000 patients with diabetes to compare the treatments. The company expects the two drugs to have an equal number of patients to indicate that both drugs are effective. It selects a significance level of 0.05, which indicates it is willing to accept a 5% chance it may reject the null hypothesis when it is true, or a 5% chance of committing a type I error.

Assume the beta is calculated to be 0.025, or 2.5%. Therefore, the probability of committing a type II error is 2.5%. If the two medications are not equal, the null hypothesis should be rejected. However, if the biotech company does not reject the null hypothesis when the drugs are not equally effective, a type II error occurs.