Define the sampling distribution of the mean.
Explain how a sampling distribution of the mean could be constructed empirically or theoretically.
Explain Central Limit Theorem.
State what the mean of the sampling distribution of the mean is equal to.
State, write, and use the formula for calculating the standard error of the mean.
Explain what the standard error of the mean is in terms of the sampling distribution of the mean.
Explain what sampling error is.
Explain what the standard error of the mean measures.
Explain when the sampling distribution of the mean is normally, or nearly normally, distributed in terms of the shape of the population and the size of the samples.
State what a Z score is.
Given the mean and s.d. of a normal distribution, calculate the z score for a given score.
Given the mean and s.d. of a population, and a sample size, calculate the z score for a given sample mean.
Given the mean and s.d. of a population, and a sample size, use a z table (or this simulation) to find the probability of getting a sample mean of a given value or greater.
Explain what z scores can be used for in terms of comparing scores from different distributions and in terms of finding the probability of getting a particular sample mean.
Explain the effect of sample size and population variability on the standard error of the mean.
Explain the effect of the size of the standard error of the mean on the accuracy of a sample mean.
Explain the importance of the sample size and the size of the population for the accuracy of the sample mean.