Statistics Fundamentals Vocabulary (Population, Sample, Data, and Sampling Methods)

Vocabulary-style flashcards covering core concepts on statistics, data types, population vs. sample, and common sampling methods and errors.

Statistics

A method for dealing with data; a tool for organizing and analyzing numerical facts or observations.

Data

Measurements or observations made on subjects; note that data is plural and datum is singular.

Datum

A single measurement or observation; the singular form of data.

Population

The complete set of individuals/objects/scores that the investigator is interested in studying.

Sample

A subset of the population used for analysis.

Statistic

A numerical value calculated from the sample data that describes a characteristic of the sample (e.g., sample mean x̄).

Parameter

A numerical value calculated from population data that describes a characteristic of the population (e.g., population mean μ).

Parameter notation

Greek letters (e.g., μ, σ) used to denote population characteristics.

Statistic notation

Roman letters (e.g., x̄, s) used to denote sample characteristics.

Biased Sample

A sample that over-represents some parts of the population and under-represents others, often not representative and can mislead conclusions.

Convenience Sampling

Selecting individuals for a sample who are easily accessible; often biased.

Random Sample

A sample where every possible sample of size n has the same chance of being selected and every member of the population has the same chance of being chosen; unbiased.

Simple Random Sampling

Each observation has an equal chance of being selected; selections are typically independent (e.g., drawing names from a hat).

Systematic Random Sampling

Randomly select a starting point and then select every k-th individual according to a rule.

Stratified Random Sampling

Divide the population into strata based on a characteristic, then take random samples from each stratum and combine.

Cluster Sampling

Divide the population into clusters (often by location), randomly select clusters, and sample individuals within those clusters.

Sampling Errors

Any error in sampling that leads to a biased sample; arises from the sampling process itself.

Non-sampling Errors

Errors not related to the act of selecting a sample (e.g., missing data, response errors, processing errors).

Missing Data

Inability to contact a subject or refusal to participate.

Response Errors

Subjects may lie or misremember information, leading to incorrect responses.

Processing Errors

Errors introduced during data entry or arithmetic calculations.