When is the t distribution the right one for inference about a mean?
Show the answer and explanation
Answer: C. When the population standard deviation is unknown
That is the usual case in practice, which is why the t-test is the workhorse.
Why the other options are wrong:
A. When the sample is small regardless of what is known - If the population standard deviation is genuinely known, the normal applies at any sample size. It is what is unknown that matters, not how much data there is.
B. When the population is not normal - Non-normality is handled by the central limit theorem and a large enough sample, not by switching to t.