Chapter 15: Sampling Distributions

These flashcards cover the key concepts and terms from Chapter 15 on Sampling Distributions, serving as a study aid for understanding statistics.

Parameter

A number that describes a population, typically unknown and estimated using statistics.

Statistic

A number computed from sample data that provides an estimate of a population parameter.

Sampling Distribution

The distribution of all possible values of a statistic obtained from samples of the same size from the same population.

Law of Large Numbers

As the sample size increases, the sample mean will tend to get closer to the population mean.

Unbiased Estimator

An estimator whose expected value is equal to the parameter it estimates, such as the sample mean x for the population mean µ.

Central Limit Theorem

The theorem stating that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the population's distribution.

Statistical Significance

An observed effect is statistically significant if it is unlikely to have occurred by chance.

Probability Distribution

A mathematical function that provides the probabilities of occurrence of different possible outcomes.

Simulation

The use of software to model random behavior and explore sampling distributions.

Variance

A measure of the dispersion of a set of values, indicating how far the values are spread out from their average.

Sample Mean (x)

The average of a sample, used as an estimate of the population mean (µ).

Population Mean (µ)

The average of all possible values in a population.

Standard Deviation (σ)

A statistic that measures the dispersion of a set of values relative to their mean.

Simulation Study

A study that uses a simulation to generate data and compare the results against theoretical expectations.