Chapter 5

Population

A group of objects or people sharing one or more researcher-defined characteristics

Parameter

Numerical values generated from a population, typically denoted using greek letters (σ for population standard deviation)

Sample

A representative subset chosen from a population to gather data that can be generalized back to the target population

Statistic

Numerical values generated from a population to gather data that can be generalized back to the target population

Representative Sample

Contains all the attributes of the population in the same proportion that they occur in the population.

Inclusion Criteria

Required characteristics needed to join the target population.

exclusion Criteria

Factors that exclude a potential subject from the sample

Sampling Method

The structured process used to select participants from a target population.

Probability Sampling Methods

Methods where every subject's probability of being selected is known, relying on randomization.

Simple Random Sampling

Every member has an equal opportunity of being chosen.

Stratified Random Sampling

The population is split into subgroups (strata) based on specific criteria, followed by random selection from each subgroup to match population ratios.

Cluster Sampling

Randomly selecting naturally occurring groups (e.g., hospitals) rather than individual subjects.

Systematic Sampling

Selecting subjects based on a standardized rule or interval (e.g., every 4th person) from an ordered list.

Nonprobability Sampling Methods

Methods where selection chances are unknown and subjects are selected non-randomly.

Convenience Sampling

Selecting readily accessible subjects ("right place at the right time").

Quota Sampling:

Non-randomly sampling subjects from specific subgroups until predefined targets are filled, stopping once reached.

Purposive Sampling:

Selecting specific subjects intentionally because they are "information-rich" cases (common in qualitative research).

Network Sampling:

Leveraging existing participants' social connections to locate hard-to-reach populations.

Sampling Error:

Differences between sample metrics and population metrics caused purely by chance

Sampling Bias:

Systematic errors in selection leading to a sample that fails to accurately represent the population.

Sampling Distribution

The distribution consisting of all possible values of a statistic across all possible samples of a given size.

Central Limit Theorem

Principle stating that a large enough sample size produces an approximately normal sampling distribution, regardless of the underlying population's shape.