What is a Type I Error?

A type I error is an error made by a researcher or analyst in statistical hypothesis testing. It occurs when a researcher incorrectly rejects the null hypothesis when it is actually true. This is sometimes referred to as a 'false positive' because the researcher has incorrectly identified something as being present when it actually isn't.

What Causes a Type I Error?

A type I error is caused by setting the significance level too low. The significance level is the probability of rejecting the null hypothesis when it is actually true. If the significance level is too low, then the researcher is more likely to reject the null hypothesis when it is actually true, which would be a type I error.

Examples of Type I Error

One example of a type I error is a medical test that incorrectly diagnoses a patient as having a disease when they are actually healthy. Another example would be a researcher incorrectly concluding that a drug is effective when it is actually ineffective.

Consequences of a Type I Error

The consequences of a type I error can be quite serious. In the medical example, a patient may receive unnecessary treatment that could have negative side effects. In the drug example, an ineffective drug may be given to people who could have been helped by a more effective treatment.

How to Avoid Type I Error?

The best way to avoid a type I error is to set the significance level properly. The significance level should be set at a level that is appropriate for the research being done. Generally, the higher the significance level, the less likely a type I error is to occur.

Conclusion

A type I error is an error made by a researcher or analyst in statistical hypothesis testing. It occurs when a researcher incorrectly rejects the null hypothesis when it is actually true. This is sometimes referred to as a 'false positive' because the researcher has incorrectly identified something as being present when it actually isn't. The best way to avoid a type I error is to set the significance level properly. The higher the significance level, the less likely a type I error is to occur.