If we reject the null hypothesis when it is true, we might be making
Answer & explanation
Correct answer: option 1
The correct answer is Option (1) → Type-I error
Rejecting the null hypothesis \(H_0\) when it is actually true is, by definition, a Type-I error. Its probability is the level of significance \(\alpha\).
Why the other options are incorrect
Option (2) Type-II error — Incorrect. A Type-II error is the opposite mistake: accepting \(H_0\) when it is false.
Option (3) Sampling error — Incorrect. Sampling error is the difference between a sample statistic and the population parameter; it is not the name of a wrong decision about \(H_0\).
Option (4) Calculation error — Incorrect. This is not one of the two types of error in testing a hypothesis.