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Also known as: Sample set, Subset
A subset selected from a larger population to infer characteristics about the whole.
DESCRIPTION
A sample is a smaller collection of observations drawn from a defined population to estimate population parameters and test hypotheses. Good sampling design reduces bias and improves statistical efficiency. Researchers choose probability or non-probability sampling methods based on objectives and constraints. Sample size is determined by desired confidence, power, and variability. Proper sampling underpins reliable analytics, A/B testing, surveys, and model validation.
ALTERNATIVE DEFINITIONS
Penn State STAT 500
A sample is a subset of the population.
Concise, foundational definition emphasizing the population–sample relationship.
Encyclopaedia Britannica
In statistics, a sample is a subset of a population used to represent the population as a whole.
Highlights representativeness as the purpose of sampling.
NIST/SEMATECH e-Handbook of Statistical Methods
A sample is a subset of a population.
Provides the definition within a broader handbook on statistical practice.
RELATED TERMS
Sampling Frame Random Sampling Stratified Sampling Sampling Distribution Sampling Error Central Limit Theorem Estimator Confidence Interval
COMPETING TERMS
RELATIONS
- Narrower: Holdout Sample Random Sample
KNOWLEDGE AREAS
TARGET AUDIENCE
Data Scientist Data Analyst BI Specialist
EXTERNAL SOURCES
- Wikipedia - Sample (statistics)
- NIST - Handbook definition and context of samples
- Penn State - Intro lesson on populations and samples
- OpenIntro Statistics - Open textbook covering sampling basics
TOP QUESTIONS
Why is a Sample important in statistics?
Samples enable inference about a population without measuring everyone. They reduce cost and time while providing reliable estimates when designed properly.
Category: Relevancy
How large should a Sample be?
Sample size depends on desired confidence, statistical power, variability, and effect size. Power analysis and pilot data inform the optimal number of observations.
Category: Requirements
What is a common mistake when choosing a Sample?
Convenience sampling that ignores representativeness often introduces bias. A rigorous sampling frame and randomization mitigate this.
Category: Mistakes
How does a Sample relate to a Population?
A sample is a subset drawn from the population to estimate population parameters. Proper sampling ensures the sample reflects key population characteristics.
Category: Example
What is the risk of using a biased Sample?
Biased samples yield misleading estimates and decisions. The resulting models or conclusions may not generalize to the population.
Category: Risks
What types of sampling create a representative Sample?
Probability methods like simple random, stratified, and cluster sampling improve representativeness and enable valid inference.
Category: Best practices
How is a Sample used in A/B testing?
Random samples are assigned to variants to estimate treatment effects. Balanced, randomized sampling supports unbiased comparisons.
Category: Application
What is the difference between a Sample and a Census?
A census measures every unit in the population. A sample measures only some units and infers to the whole.
Category: Misunderstanding
Where can I learn more about good sampling practice?
Consult NIST’s e-Handbook, OpenIntro Statistics, and university courses like Penn State STAT for practical guidance and examples.
Category: Resources