Type I Error And Type II Error

Type I Error And Type II Error
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Type I Error And Type II Error
Type I error is also called a false positive which is the error of rejecting a null hypothesis when in fact it is true. False positive can also be described as accepting the alternative hypothesis when the findings are as a result of chance. A type I error takes place when one sees a statistically significant difference when in reality there is no difference. The likelihood of creating a type I error in a test with rejection region R is P (R | H0 is correct) (Mertler & Reinhart, 2016). On the other hand, type II error is called a false negative which is the error of accepting a null hypothesis when in fact it is false. A different definition of false negative is rejecting the alternative hypothesis when it is true. The type II error is when the alternative hypothesis is accepted, and there the difference is not identified when there is one. The likelihood of the occurrence of a type II error in a test with rejection region R is 1 – P (R | Ha is correct) (Mertler & Reinhart, 2016).
One of the goals of data analysis is to minimize the likelihood of making type I and type II. Type I error is set as 0.05 or 0.01, the level of significance or p-value, which implies there is a 5 or 1 in 100 chance that the difference is as a result of chance. Unfortunately, there isn’t a way to ensure that 5 or 1 in 100 is the correct significance level required. Therefore, the best way to reduce the likelihood of making a type I error and type II error is to increase the sample and the effect sizes.

Also, increasing the alpha value, or using a one-tailed test instead of a two-tailed test (Sullivan & Feinn, 2012).
References
Mertler, C. A., & Reinhart, R. V. (2016). Advanced and multivariate statistical methods: Practical application and interpretation. Taylor & Francis.
Sullivan, G. M., & Feinn, R. (2012). Using effect size—or why the P value is not enough. Journal of graduate medical education, 4(3), 279-282.