Which statement best describes a Type I error in hypothesis testing?

Which statement best describes a Type I error in hypothesis testing?

• A. Failing to reject the null when it is true.
• B. Rejecting the null when it is false.
• C. Rejecting the null when it is true.
• D. Failing to reject the null when it is false.

Answer: (C)

Type I error is a false positive: you conclude there is an effect or difference when there really isn’t one. It happens because of sampling variability, and the risk is set by the significance level (alpha). If alpha is 0.05, about 5% of the time you would reject a true null hypothesis purely by chance in repeated experiments. This error leads to thinking a treatment works or a difference exists when it does not, which can misguide decisions and waste resources. The alternative statements describe either a correct decision (not rejecting a true null) or a different error: failing to detect a real effect when there is one (Type II error). Rejecting the null when it is false is the correct action and reflects adequate study power.

Type I error is a false positive: you conclude there is an effect or difference when there really isn’t one. It happens because of sampling variability, and the risk is set by the significance level (alpha). If alpha is 0.05, about 5% of the time you would reject a true null hypothesis purely by chance in repeated experiments. This error leads to thinking a treatment works or a difference exists when it does not, which can misguide decisions and waste resources. The alternative statements describe either a correct decision (not rejecting a true null) or a different error: failing to detect a real effect when there is one (Type II error). Rejecting the null when it is false is the correct action and reflects adequate study power.