Abstract: Type I and Type II errors are special cases of inferential mistakes that can occur when testing a specific hypothesis. For any given statistical test, the significance level established represents the probability of committing a Type I error. One strategy for reducing Type I errors would be to use more conservative a levels. A second strategy to reduce the likelihood of making a Type I error is related to multiple hypothesis testing on the same data. One way to reduce the likelihood of Type II errors is to select more conservative p levels, such as 0.10, which will reduce the probability of Type II error to 10% and increase statistical power. As with Type I error, there are several general strategies that can be used to increase the statistical power of a given study beyond the use of more conservative p levels.
Publication Year: 2021
Publication Date: 2021-08-20
Language: en
Type: other
Indexed In: ['crossref']
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot