Chapter 8 – Sampling Distributions

This set of flashcards covers key concepts related to sampling distributions, including definitions of statistical terms and important theorems.

Sampling Distribution

A probability distribution of a statistic obtained by selecting all possible samples of a specific size from a population.

Sample Mean (𝑥̅)

The average of a set of sample values, used to estimate the population mean (𝜇).

Central Limit Theorem

States that if the sample size is large enough, the sampling distribution of the sample mean will be approximately normally distributed regardless of the population's distribution.

Standard Error (𝜎𝑥̅)

The standard deviation of the sampling distribution of the sample mean, reflecting how much the sample mean varies from sample to sample.

Sample Proportion (𝑝̂)

The proportion of successes in a sample, used to estimate the population proportion (p).

Probability Distribution

A function that describes the likelihood of obtaining the possible values of a random variable.

Independence Assumption

The requirement that the outcomes in a sample must not affect each other's probabilities, often checked by ensuring the sample size is no more than 5% of the population size.

Mean (𝜇)

The average value of a population.

Standard Deviation (𝜎)

A measure of the amount of variation or dispersion of a set of values.

Z-score

A statistic that describes a value's relationship to the mean of a group of values, expressed in terms of standard deviations.