Contents
What is a Type 2 error also called?
A type II error is a statistical term used within the context of hypothesis testing that describes the error that occurs when one accepts a null hypothesis that is actually false. A type II error produces a false negative, also known as an error of omission.
How does P value relate to Type 1 and Type 2 errors?
For example, a p-value of 0.01 would mean there is a 1% chance of committing a Type I error. However, using a lower value for alpha means that you will be less likely to detect a true difference if one really exists (thus risking a type II error).
Where do type I and Type II errors occur?
Type I and Type II errors occur where these two distributions overlap. The blue shaded area represents alpha, the Type I error rate, and the green shaded area represents beta, the Type II error rate. By setting the Type I error rate, you indirectly influence the size of the Type II error rate as well.
How is the type II error rate calculated?
The Type II error rate is beta (β), represented by the shaded area on the left side. The remaining area under the curve represents statistical power, which is 1 – β. Increasing the statistical power of your test directly decreases the risk of making a Type II error.
How to reduce the type I error probability?
To reduce the Type I error probability, you can simply set a lower significance level. The null hypothesis distribution curve below shows the probabilities of obtaining all possible results if the study were repeated with new samples and the null hypothesis were true in the population. At the tail end, the shaded area represents alpha.
What should the significance level be for a type 1 error?
The green (rightmost) curve is the sampling distribution assuming the specific alternate hypothesis “µ =1”. The choice of significance level should be based on the consequences of Type I and Type II errors. If the consequences of a type I error are serious or expensive, then a very small significance level is appropriate.