Type I Error
128 Views · Updated December 5, 2024
The term type I error is a statistical concept that refers to the incorrect rejection of an accurate null hypothesis. Put simply, a type I error is a false positive result. Making a type I error often can't be avoided because of the degree of uncertainty involved. A null hypothesis is established during hypothesis testing before a test begins. In some cases, a type I error assumes there's no cause-and-effect relationship between the tested item and the stimuli to trigger an outcome to the test.
Definition
A Type I error is a statistical concept that refers to the incorrect rejection of a true null hypothesis. Simply put, a Type I error is a false positive result.
Origin
The concept of Type I error originates from the theory of hypothesis testing in statistics. Hypothesis testing was developed by statistician Ronald Fisher in the early 20th century to determine whether data supports a particular hypothesis.
Categories and Features
Type I errors are typically associated with the significance level (α), which is the probability of committing a Type I error that researchers are willing to accept. Common significance levels include 0.05, 0.01, etc. The feature of Type I error is that it is unavoidable because any statistical test can have errors due to random sampling.
Case Studies
In drug trials, if the null hypothesis is that a drug is ineffective, but the trial results show it is effective, this is a Type I error. Another example is in quality control, where the null hypothesis is that a batch of products is acceptable, but the test results show it is not, which is also a Type I error.
Common Issues
Investors might misunderstand a Type I error as a failure of the test, whereas it is an inherent risk in statistical testing. A common misconception is that Type I errors can be completely eliminated, but in reality, they can only be controlled by adjusting the significance level.
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