Why do we prefer the t procedures to the z procedures for inference about a population mean? Group of answer choices z requires that you know the population standard deviation σ which may be unrealistic. z can be used only for large samples. t does not require your data to be a random sample from the population. z requires that you know the population mean

Correct option:

"z requires that you know the population standard deviation σ which may be unrealistic."

Step-by-step explanation:

The hypothesis test for significant population mean μ can be done either using the z-distribution of t-distribution.

Both the distribution require certain conditions to be fulfilled to use.

For using a z-distribution to perform a hypothesis test for μ the conditions to fulfilled are:

Population is Normally distributed. The population standard deviation is known. The sample selected is large.

For using a t-distribution to perform a hypothesis test for μ the conditions to fulfilled are:

Population is Normally distributed. The sample selected is randomly selected.

If the population standard deviation is not known and we have to compute the sample standard deviation then use the t-distribution to perform the test for population mean.

Thus, the correct option is:

"z requires that you know the population standard deviation σ which may be unrealistic."