Final answer:
In hypothesis testing, a Type I error occurs when the null hypothesis is incorrectly rejected even though it's true, which corresponds to option (a).
Step-by-step explanation:
Understanding Type I and Type II Errors
When conducting hypothesis testing in statistics, it's possible to make two main types of errors: Type I error and Type II error. A Type I error occurs when the null hypothesis is incorrectly rejected despite being true. This is the equivalent of a false positive in diagnostic testing—claiming there is an effect or a difference when there isn't one. The probability of committing a Type I error is denoted by the Greek letter alpha (α).
On the other hand, a Type II error happens when the null hypothesis is not rejected even though it is false. In other words, this error signifies a false negative, indicating there is no effect or difference when in fact there is. The probability of committing a Type II error is symbolized by the Greek letter beta (β).
Answering the student's question, a Type I error is when the null hypothesis is incorrectly rejected when it is true. So, the correct answer is option (a).