Sampling Distributions

These flashcards cover key concepts related to sampling distributions, probability distributions, and characteristics of sample means and proportions, based on lecture notes.

Sampling Distribution of the Sample Mean

The probability distribution of all possible values of the sample mean computed from a sample of size n.

Central Limit Theorem

States that regardless of the population distribution shape, the sampling distribution of the sample mean approaches normality as the sample size increases.

IQ (Intelligence Quotient)

A measurement of intelligence derived from standardized tests, with an approximate normal distribution, mean 100, and standard deviation 15.

Standard Error of the Mean

The standard deviation of the sampling distribution of the sample mean, often represented as σx̄.

Normal Population Distribution

A distribution where data forms a bell-shaped curve, characterized by its mean and standard deviation.

Nonnormal Population Distribution

A distribution that does not have the properties of a normal distribution, often skewed.

Shape of the Distribution of Sample Means

As sample size n increases, the distribution of the sample mean becomes approximately normal.

Sample Proportion (p̂)

A statistic estimating the population proportion, calculated as the number of successes divided by the total sample size.

Probability Distribution

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

Statistical Inference

The process of using data from a sample to make conclusions about a population.