# What is Type I Error and Type II Error in Statistics?

Type I Error (False Positive Error)

A type I error occurs when the null hypothesis is true, but is rejected. Let me say this again, type I error occurs when the null hypothesis is actually true , but was rejected as false by the testing.

A type I error, or false positive, is asserting something as true when it is actually false. This false positive error is basically a “false alarm” – a result that indicates a given condition has been fulfilled when it actually has not been fulfilled (i.e., erroneously a positive result has been assumed).

Type II Error (False Negative)

A type II error occurs when the null hypothesis is false, but erroneously fails to be rejected. Let me say this again, type II error occurs when the null hypothesis is actually false , but was accepted as true by the testing.

A type II error, or false negative, is where a test result indicates that a condition failed, while it actually was successful. A Type II error is committed when we fail to believe a true condition.