Given the following null hypothesis, give an example of a Type I error.
H0: There is no difference between the number of males or females who go to their primary care physician for an annual exam.
Rejecting Ho when it is actually true.
reject the null hypothesis even though it is true
A Type I error occurs when the null hypothesis is incorrectly rejected. In the given example, a Type I error would be made if we were to reject the null hypothesis, suggesting that there is a difference between the number of males and females who go to their primary care physician for an annual exam when in reality there is no difference.
For instance, if a study were to incorrectly find that more males than females go for annual exams when there is no actual difference, that would be an example of a Type I error.
To answer this question, let's first understand the concept of Type I error. In statistical hypothesis testing, a Type I error occurs when the null hypothesis (H0) is incorrectly rejected, suggesting there is a difference or effect when, in fact, it does not exist.
In the null hypothesis given (H0: There is no difference between the number of males or females who go to their primary care physician for an annual exam), it assumes that there is no difference between males and females in terms of seeking annual exams from their primary care physicians.
An example of a Type I error in this scenario would be if a statistical analysis data indicates a significant difference between the number of males and females who go for annual exams, leading us to reject the null hypothesis and conclude that there is a difference. However, in reality, there is actually no difference between males and females in their frequency of seeking annual exams.
It is important to note that the likelihood of committing a Type I error is determined by the significance level (alpha) chosen for the hypothesis test. Commonly used alpha levels are 0.05 or 0.01, which represent a 5% or 1% chance of rejecting the null hypothesis when it is true, respectively.