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A sample selected so each member of the population has an equal chance of inclusion.
DESCRIPTION
A random sample underpins statistical inference by ensuring selection is governed by chance. In simple random sampling, each population unit has equal probability of selection, reducing selection bias. Random sampling supports unbiased estimation, valid confidence intervals, and hypothesis tests. Practical challenges include constructing a complete sampling frame and handling nonresponse. Variants include stratified, cluster, and systematic sampling.
ALTERNATIVE DEFINITIONS
NIST/SEMATECH e-Handbook of Statistical Methods
In simple random sampling, each item in the population has the same probability of being selected.
Defines the equal-probability principle central to random sampling.
Wikipedia
A simple random sample (SRS) is a subset of individuals chosen from a larger set, in which each individual is chosen randomly and entirely by chance.
Emphasizes chance-based selection from a larger set.
RELATED TERMS
Sampling Simple Random Sample Cluster Sampling Sampling Frame Sampling Bias Central Limit Theorem Confidence Interval A/B Testing
COMPETING TERMS
Convenience Sample Stratified Sampling
RELATIONS
- Broader: Sample
KNOWLEDGE AREAS
Data Science Mathematics Testing & Validation
TARGET AUDIENCE
Data Scientist Data Analyst Information Analyst AI Product Manager
EXTERNAL SOURCES
- Wikipedia - Simple random sample
- NIST - Authoritative overview of simple random sampling
- Penn State Eberly College of Science - Course notes on simple random sampling
TOP QUESTIONS
Why is a Random Sample important?
It reduces selection bias, enabling unbiased estimation and valid inference about the population.
Category: Relevancy
How do I generate a Random Sample in practice?
Use RNGs with a defined seed and a complete sampling frame; avoid convenience selection.
Category: Requirements
What is the risk of an incomplete sampling frame?
It introduces coverage bias, making the sample unrepresentative of the population.
Category: Risks
How large should a Random Sample be?
Sample size depends on desired margin of error, variance, and confidence level; use power calculations.
Category: Requirements
Is Random Sampling always better than Stratified Sampling?
Not always. Stratification can reduce variance when meaningful strata exist.
Category: Opinion
How does nonresponse affect a Random Sample?
Nonresponse can reintroduce bias; weighting and follow-ups help mitigate it.
Category: Risks
What is the biggest misunderstanding about Random Samples?
Random does not mean haphazard; it requires a formal chance mechanism and a proper frame.
Category: Misunderstanding
How are Random Samples used in A/B testing?
Users are randomly sampled and assigned to ensure comparable groups and unbiased effect estimates.
Category: Example
Can Random Sampling be combined with clustering?
Yes. Cluster sampling randomly selects groups, then units within clusters for efficiency.
Category: Example