Type I and Type II Errors: A Type I error occurs when we reject the null hypothesis when it is true. In other words, we conclude that the difference bet...
Type I and Type II Errors:
A Type I error occurs when we reject the null hypothesis when it is true. In other words, we conclude that the difference between two groups is significant when it is not actually significant. This means that we made a false positive error.
A Type II error occurs when we fail to reject the null hypothesis when it is false. In other words, we conclude that the difference between two groups is not significant when it actually is significant. This means that we made a false negative error.
Type I and Type II errors are inversely related. This means that if we decrease the probability of making a Type I error, we increase the probability of making a Type II error. Conversely, if we decrease the probability of making a Type II error, we increase the probability of making a Type I error.
Examples:
If we are testing the claim that the average height of women is equal to 6 feet, and we reject this claim based on a Type I error, we might conclude that there is no difference between the average heights of men and women.
If we are testing the claim that a new drug is safe, and we fail to reject this claim based on a Type II error, we might conclude that the drug is safe when it is actually unsafe.
Understanding type I and type II errors is important for making accurate decisions about a population when there is limited information