What is the difference between a Type I and Type II error?

A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population.

What are type and type II errors?

In statistical hypothesis testing, a type II error is a situation wherein a hypothesis test fails to reject the null hypothesis that is false.

What is Type I and Type II error in classification?

Basically, the Type I error occurs when the null hypothesis is true and your ML model rejects it (false positive). The Type II error occurs when the null hypothesis is false and it does not reject it (false negative). Therefore, the “risks” of these two errors are inversely related.

What is an example of a Type I error?

Examples of Type I Errors For example, let’s look at the trial of an accused criminal. The null hypothesis is that the person is innocent, while the alternative is guilty. A type I error in this case would mean that the person is not found innocent and is sent to jail, despite actually being innocent.

Which is worse Type I or Type II error?

A type II error occurs when the null hypothesis is false but still not rejected, also known as a false negative. Type I error is considered to be worse or more dangerous than type II because to reject what is true is more harmful than keeping the data that is not true.

What causes a Type 2 error?

Type II error is mainly caused by the statistical power of a test being low. A Type II error will occur if the statistical test is not powerful enough. The size of the sample can also lead to a Type I error because the outcome of the test will be affected.

Are Type 1 and Type 2 errors inversely related?

Anytime we make a decision using statistics there are four possible outcomes, with two representing correct decisions and two representing errors. The chances of committing these two types of errors are inversely proportional: that is, decreasing type I error rate increases type II error rate, and vice versa.

What causes type II error?

Is Type I or type II error worse?

Why is Type 1 and Type 2 errors important?

As you analyze your own data and test hypotheses, understanding the difference between Type I and Type II errors is extremely important, because there’s a risk of making each type of error in every analysis, and the amount of risk is in your control.