The extent to which sample statistics vary from sample to sample.

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

Standard error measures the variability of the sample statistic. It is the standard deviation of the sampling distribution.

Population parameters are fixed values that describe characteristics of an entire population, such as the population mean ($\mu$) or population standard deviation ($\sigma$).

Sample statistics are estimates of population parameters calculated from a sample, such as the sample mean ($\bar{x}$) or sample standard deviation (s).

Discrete: Finite or countable values, e.g., number of students | Continuous: Any value within a range, e.g., height.

Parameters: Describe populations, fixed values | Statistics: Describe samples, estimates of parameters.

A distribution of a statistic calculated from all possible samples of a given size from a population.

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

The standard deviation of a sampling distribution; it measures the variability of the sample statistic.

Variables that can only take on a finite number of values or a countable number of values.

Variables that can take on any value within a given range.

A measure that describes a characteristic of an entire population; it's a fixed, true value.

A measure that describes a characteristic of a sample; it's an estimate of the population parameter.