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:

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

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."

just do 5.2 + 2 ( 7.2) and 1/3 x 3 (.9) and youv'e got your equation. 0.9( 9/10) + 7.2 ^2 = 16.4 hope i could !

step-by-step explanation: