a. a sampling distribution is the distribution of all possible values of a select calculated from a…

a. a sampling distribution is the distribution of all possible values of a select calculated from a particular sample size, assuming the select.\nb. if hypotheses are about a the population mean, mu, then the statistic we use in the hypothesis test is the select.\nc. the sampling distribution of the sample mean is select as long as certain conditions are met.\nd. when we are using the sample standard deviation as an approximation of the population standard deviation, then the necessary conditions for the distribution of all possible means to be approximately normal are: the sample must have been collected select, the population size must be select, and the sample size must be select.\ne. the p - value for a test for the population mean mu is calculated from the select of the select.\nf. the p - value is the chance of getting the sample mean value select or select if the population mean value is select.

a. a sampling distribution is the distribution of all possible values of a select calculated from a particular sample size, assuming the select.\nb. if hypotheses are about a the population mean, mu, then the statistic we use in the hypothesis test is the select.\nc. the sampling distribution of the sample mean is select as long as certain conditions are met.\nd. when we are using the sample standard deviation as an approximation of the population standard deviation, then the necessary conditions for the distribution of all possible means to be approximately normal are: the sample must have been collected select, the population size must be select, and the sample size must be select.\ne. the p - value for a test for the population mean mu is calculated from the select of the select.\nf. the p - value is the chance of getting the sample mean value select or select if the population mean value is select.

Answer

Explanation:

Step1: Define sampling distribution

A sampling distribution is the distribution of all possible values of a statistic calculated from a particular sample size, assuming the sampling is random.

Step2: Identify test - statistic for population mean hypothesis

If hypotheses are about the population mean $\mu$, then the statistic we use in the hypothesis test is the sample mean $\bar{x}$ (in a one - sample z - test or t - test depending on known or unknown population standard deviation).

Step3: Sampling distribution of sample mean property

The sampling distribution of the sample mean is approximately normal as long as certain conditions are met.

Step4: Conditions for normal approximation with sample s.d.

When we are using the sample standard deviation as an approximation of the population standard deviation, then the necessary conditions for the distribution of all possible means to be approximately normal are: the sample must have been collected randomly, the population size must be large (ideally infinite), and the sample size must be n ≥ 30 (Central Limit Theorem).

Step5: p - value calculation

The p - value for a test for the population mean $\mu$ is calculated from the probability of the test - statistic value.

Step6: p - value interpretation

The p - value is the chance of getting the sample mean value as extreme as or more extreme than if the population mean value is the hypothesized value.

Answer:

a. statistic b. sample mean $\bar{x}$ c. approximately normal d. randomly; large (ideally infinite); n ≥ 30 e. probability; test - statistic f. as extreme as; more extreme than; the hypothesized value