In a hypothesis test, the null hypothesis is rejected, and it is later determined that the null hypothesis was actually true. What type of error has been made? A. Sampling error, as the sample did not represent the population accurately. B. Type Il error, as the null hypothesis should have been rejected. C. Type I error, as the null hypothesis was incorrectly rejected. D. Statistical error, as the level of significance was set too high.

You've stumbled upon the classic Type I error! This occurs when you reject the null hypothesis when it is true, making a false claim that something significant has occurred when it hasn't. It's like thinking you hear a fire alarm and running out of the building, only to realize it was just a false alarm! Understanding this type of error is crucial for researchers, as it underscores the importance of setting an appropriate significance level (alpha). Ideally, we want to minimize the chances of making this error, often represented with an alpha value of 0.05. This means we accept a 5% chance of wrongly rejecting a true null hypothesis—a balancing act between finding real effects and avoiding false claims!