🎲Random Sampling

NEW

Also known as: Simple Random Sampling

Selecting a subset of a population so that each member or sample has an equal chance of inclusion.

DESCRIPTION

Random sampling selects units from a population in a way that ensures unbiased, independent inclusion, enabling valid statistical inference. In simple random sampling, every unit or every sample of size n has equal probability. It underpins confidence intervals, hypothesis tests, and A/B testing. Proper sampling frames and randomization mechanisms are essential to avoid selection bias. Random sampling supports generalizability and reduces systematic error compared to convenience sampling.

ALTERNATIVE DEFINITIONS

NIST/SEMATECH e-Handbook of Statistical Methods

A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen.

Emphasizes equal probability for each population member in simple random sampling.

Source

Penn State Statistics Online

In simple random sampling, each sample of size n has an equal probability of being selected.

Focuses on equal probability across all possible samples of a given size.

Source

RELATED TERMS

Sampling Frame Sampling Bias Central Limit Theorem Bootstrap Sampling Confidence Interval A/B Testing Cross-Validation Monte Carlo Simulation

COMPETING TERMS

Stratified Sampling Systematic Sampling

RELATIONS

• Broader: Sampling

KNOWLEDGE AREAS

Data Science Mathematics Testing & Validation Data Quality

TARGET AUDIENCE

Data Scientist Data Analyst Information Analyst Data Steward Data Engineer

EXTERNAL SOURCES

• Wikipedia - Simple random sample
• Penn State Statistics Online - Course notes on simple random sampling
• NIST - Official handbook definition and guidance

TOP QUESTIONS

Why is Random Sampling important?

Random Sampling reduces selection bias and enables valid inference about a population, supporting estimates, confidence intervals, and hypothesis tests.

Category: Relevancy

What is required to perform Random Sampling?

You need a reliable sampling frame, a randomization mechanism (e.g., RNG), and clear inclusion criteria.

Category: Requirements

What are common mistakes in Random Sampling?

Using incomplete frames, predictable pseudo-random procedures, or replacing missing units without re-randomization introduces bias.

Category: Mistakes

How large should a Random Sampling sample be?

Determine size using power analysis or margin-of-error formulas, considering variability, effect size, and acceptable error.

Category: Practical

How does Random Sampling differ from Stratified Sampling?

Random Sampling draws uniformly from the population; stratified sampling divides the population into strata and samples within each to control variance.

Category: Comparison

When should I avoid Random Sampling?

Avoid pure random sampling when key subgroups are rare or must be represented; consider stratified or oversampling strategies.

Category: Advice

Is Random Sampling the same as Random Assignment?

No, sampling selects units for a study; random assignment allocates sampled units to treatments.

Category: Misunderstanding

How does Random Sampling affect confidence intervals?

True random sampling justifies standard error estimates and confidence intervals that rely on independent, identically distributed draws.

Category: Concept

What tools support Random Sampling in practice?

Statistical packages (R, Python, SQL) and survey tools provide RNGs and sampling functions like sample(), RAND(), or TABLESAMPLE.

Category: Tools