Sampling Distribution and Estimation

This set of flashcards covers key concepts and terminology related to sampling distributions and estimation in research methods.

Sampling Distribution

The distribution of sample means based on random samples drawn from a population.

Central Limit Theorem (CLT)

States that the means of a sufficiently large number of samples of a population will be normally distributed.

Probability Sampling

A technique where every member of the population has an equal chance of being included in the sample.

Simple Random Sampling

A probability sampling design where each member of the population has an equal chance of selection.

Stratified Random Sampling

A probability sampling technique that divides the population into strata to ensure representation.

Cluster Sampling

A sampling technique that involves dividing the population into clusters and randomly selecting entire clusters.

Systematic Sampling

A method of sampling in which members are selected at regular intervals from an ordered list.

Non-Probability Sampling

A sampling method where not all individuals have a chance of being selected.

Convenience Sampling

Selecting participants who are easily accessible rather than randomly chosen.

Quota Sampling

Non-probability technique ensuring representation of specific characteristics in the sample.

Purposeful Sampling

Selecting individuals purposefully based on specific characteristics or criteria.

Network Sampling

A method in which existing study subjects recruit future subjects from among their acquaintances.

Theoretical Sampling

Choosing participants that will help develop a theoretical framework in qualitative research.

Validity in Sampling Methods

Refers to how accurately the sample represents the population.

Reliability in Sampling Methods

The consistency of a sampling method in selecting participants across repeated studies.