Type I error represents?

Type I error represents?

• A. A true positive
• B. A true negative
• C. A false positive (rejecting a true null hypothesis)
• D. A false negative (failing to reject a false null hypothesis)

Answer: (C)

In hypothesis testing, a Type I error happens when you reject the null hypothesis even though it is actually true. The null hypothesis usually states that there is no effect or no difference. So this error is like a false alarm: you conclude there is an effect when there really isn’t one. This is described as a false positive, and its likelihood is controlled by the significance level, alpha. For comparison, a true positive means you correctly detect an effect when there is one, a true negative means you correctly conclude there is no effect when there isn’t one, and a false negative means you fail to detect an actual effect by not rejecting a false null.

In hypothesis testing, a Type I error happens when you reject the null hypothesis even though it is actually true. The null hypothesis usually states that there is no effect or no difference. So this error is like a false alarm: you conclude there is an effect when there really isn’t one. This is described as a false positive, and its likelihood is controlled by the significance level, alpha. For comparison, a true positive means you correctly detect an effect when there is one, a true negative means you correctly conclude there is no effect when there isn’t one, and a false negative means you fail to detect an actual effect by not rejecting a false null.