Chapter 5 - Hypothesis Tests with Mean of Samples

Distribution of Means

A distribution of the means of lots of samples of a given size from a population (also called a sampling distribution of the mean); comparison distribution when testing hypotheses involving a single sample of more than one individual.

Mean of a Distribution of Means

This is the average of all the sample means you would get if you took infinitely many samples from a population; it comes out to be the same as the mean of the population of individuals.

μ_M

Means of a distribution of means

Variance of a Distribution of Means

Variance of the population divided by the number of scores in each sample.

σ²_M

Variance of a distribution of means

Standard Deviation of a Distribution of Means

Square root of the variance of a distribution of means; also called standard error of the mean (SEM) and standard error (SE)

σ_M

Standard deviation of a distribution of means

Standard error of the mean (SEM)

Same as standard deviation of a distribution of means; also called standard error (SE).

Standard Error (SE)

A measure of how much the mean of a sample likely differs from the mean of the entire population. Same as standard deviation of a distribution of means. AKA “SEM”

Z test

Hypothesis-testing procedure in which there is a single sample and the population variance is known.

Confidence Interval (CI)

Roughly speaking, the range of scores (that is, the scores between an upper and lower value) that is likely to include the true population mean; more precisely, the range of possible population means from which it is not highly unlikely that you could have obtained your sample mean.

Confidence Limit

Upper or lower value of a confidence interval.