What’s the type I and type II errors?
Type I and Type II errors are two types of statistical errors that can occur in hypothesis testing.
Type I error occurs when the null hypothesis (H0) is rejected even though it is true. It means that the test incorrectly concludes that there is a significant difference or effect when in reality there is none. Type I error is also called a false positive.
Type II error occurs when the null hypothesis (H0) is not rejected even though it is false. It means that the test incorrectly concludes that there is no significant difference or effect when in reality there is one. Type II error is also called a false negative.
In simpler terms, a Type I error is the mistake of thinking something is true when it is not, while a Type II error is the mistake of thinking something is not true when it actually is.
To minimize the chances of both types of errors occurring, statisticians often use measures like power analysis to determine the sample size and significance level of their study.
