Type I error occurs in which situation?

Type I error occurs in which situation?

• A. Occurs when we believe that there is a genuine effect in the population, when in fact there isn't.
• B. Occurs when we fail to detect a real effect.
• C. Occurs when data are not normally distributed.
• D. Occurs when the sample size is too large.

Answer: (A)

In hypothesis testing, a Type I error happens when you reject the null hypothesis even though it is true. In other words, you conclude there is an effect in the population when there really isn’t one—this is a false positive. The chance of making this error is what researchers set with the alpha level (often 0.05). That first statement captures this idea precisely: believing there is a genuine effect when there isn’t. The other options describe different ideas: failing to detect a real effect is a Type II error (a false negative). Data not being normally distributed or having too large a sample size relate to other concerns (assumptions and power), not to the specific error type defined by deciding to reject the null.

In hypothesis testing, a Type I error happens when you reject the null hypothesis even though it is true. In other words, you conclude there is an effect in the population when there really isn’t one—this is a false positive. The chance of making this error is what researchers set with the alpha level (often 0.05).

That first statement captures this idea precisely: believing there is a genuine effect when there isn’t. The other options describe different ideas: failing to detect a real effect is a Type II error (a false negative). Data not being normally distributed or having too large a sample size relate to other concerns (assumptions and power), not to the specific error type defined by deciding to reject the null.