## What is a Type 1 error in hypothesis testing?

A type I error is a kind of fault that occurs during the hypothesis testing process when a null hypothesis is rejected, even though it is accurate and should not be rejected. In hypothesis testing, a null hypothesis is established before the onset of a test. These false positives are called type I errors.

## What do you mean by Type 1 and Type 2 error?

In statistics, a Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s actually false. This means that your results only have a 5% chance of occurring, or less, if the null hypothesis is actually true.

**What is a Type 2 error in hypothesis testing?**

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. A type II error produces a false negative, also known as an error of omission.

### What is a Type 1 error rate?

A type 1 error is also known as a false positive and occurs when a researcher incorrectly rejects a true null hypothesis. A p-value of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis.

### What is the probability of Type II error?

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.

**What is the probability of Type I error?**

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.

#### What does type 1 and Type 2 error mean?

Type I error is an error that takes place when the outcome is a rejection of null hypothesis which is, in fact, true. Type II error occurs when the sample results in the acceptance of null hypothesis, which is actually false.

#### When is a type I error committed?

A Type 1 error is commtted if we reject the null hypothesis when it is true. A Type 2 error is committed if we accept the null hypothesis when it is false.