Type I and type II errors
In statistics, type I and type II errors are errors that happen when a coincidence occurs while doing statistical inference, which leads to one making the wrong conclusion. One makes a Type I error when the null hypothesis is rejected, when it is actually true. Conversely, one makes a Type II error when the null hypothesis is accepted, when it is actually false. The probability of type I error is often written as [math]\displaystyle{ \alpha }[/math], while the probability of type II error is written as [math]\displaystyle{ \beta }[/math].[1][2][3]
Type I And Type II Errors Media
The results obtained from negative sample (left curve) overlap with the results obtained from positive samples (right curve). By moving the result cutoff value (vertical bar), the rate of false positives (FP) can be decreased, at the cost of raising the number of false negatives (FN), or vice versa (TP = True Positives, TPR = True Positive Rate, FPR = False Positive Rate, TN = True Negatives).
An illustration in a law review article by the Hon. James A. Shapiro and Prof. Karl T. Muth explains Type I and Type II errors in the criminal law context.
Related pages
References
- ↑ Greek/Hebrew/Latin-based Symbols in Mathematics (in en-US). Math Vault (2020-03-20). Retrieved 2020-10-03.
- ↑ 5. Differences between means: type I and type II errors and power | The BMJ. www.bmj.com. Retrieved 2020-10-03.
- ↑ Banerjee, Amitav. Hypothesis testing, type I and type II errors. Industrial Psychiatry Journal 18 (2) (2009). p. 127–131. doi:10.4103/0972-6748.62274.