A Type I error occurs when

A Type I error occurs when

• A. There is no effect but you conclude there is.
• B. There is an effect and you miss it.
• C. There is an effect and you correctly detect it.
• D. There is no effect and you correctly fail to reject it.

Answer: (A)

Type I error happens when you reject the null hypothesis even though it’s true. The null means there is no real effect. If the data lead you to conclude there is an effect when, in reality, there isn’t one, you’ve made a Type I error—a false positive. The scenario described matches this exactly: there is no effect, but you conclude there is. To place it in context, other possibilities include a Type II error (there is an effect, but you miss it), a true negative (no effect and you correctly fail to reject), and a true positive (there is an effect and you correctly detect it). The chance of making a Type I error is determined by the chosen significance level, alpha (for example, 0.05).

Type I error happens when you reject the null hypothesis even though it’s true. The null means there is no real effect. If the data lead you to conclude there is an effect when, in reality, there isn’t one, you’ve made a Type I error—a false positive. The scenario described matches this exactly: there is no effect, but you conclude there is.

To place it in context, other possibilities include a Type II error (there is an effect, but you miss it), a true negative (no effect and you correctly fail to reject), and a true positive (there is an effect and you correctly detect it). The chance of making a Type I error is determined by the chosen significance level, alpha (for example, 0.05).