Lecture 12: Characteristics of the Sample Mean

Flashcards covering key concepts related to inferential statistics and sampling distributions.

Inferential Statistics

A branch of statistics that allows making conclusions about a population based on sample data.

Sampling Error

The difference between the sample mean and the population mean.

Sampling Distribution

A distribution of statistics obtained from all possible samples of a specific size (n) from the same population.

Central Limit Theorem

States that regardless of the population distribution, the sampling distribution of the mean will approach a normal distribution as the sample size increases.

Standard Error of the Mean (SEM)

The standard deviation of the sampling distribution of the mean, calculated as the population standard deviation divided by the square root of the sample size.

Population Parameter

A value, such as a mean or standard deviation, that describes a characteristic of a population.

Sample Statistic

A value, such as a mean or standard deviation, calculated from a sample which estimates a population parameter.

Z-Transformation

A statistical method that converts a sample mean to a z score in order to determine the probability of obtaining that mean from a population with a known mean and standard deviation.

Normal Distribution

A type of continuous probability distribution for a real-valued random variable, characterized by its bell shape and defined by its mean and standard deviation.

Variability

A measure of the extent to which sample means differ from one another, related to the standard deviation of the sampling distribution.