🎯Sampling

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Also known as: Statistical Sampling

Selecting a subset of units from a larger population to infer characteristics of the whole.

DESCRIPTION

Sampling is the foundation of statistical inference, enabling conclusions about populations without measuring every unit. Probability sampling methods (e.g., simple random, stratified, cluster) provide quantifiable error bounds and support unbiased estimation. Nonprobability methods (e.g., convenience, quota) can be practical but risk bias. Sample size planning balances precision, variance, and cost. The central limit theorem underpins many interval estimates and hypothesis tests using sample statistics. Sampling is pervasive in surveys, A/B testing, quality control, and machine learning (e.g., minibatches). Careful design, randomization, and stratification improve representativeness and reduce sampling bias. Weighting and post-stratification can correct imbalances after collection.

ALTERNATIVE DEFINITIONS

Wikipedia

In statistics, sampling is the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population.

Emphasizes sampling as a means to estimate population characteristics.

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Investopedia

Sampling is a process used in statistical analysis in which a predetermined number of observations are taken from a larger population.

Highlights predetermined observation count for statistical analysis.

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OECD Glossary of Statistical Terms

Sampling is the selection of a subset of units from a population of interest to represent the population.

Frames sampling as representation in official statistics context.

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RELATED TERMS

Sampling Bias Random Sampling Stratified Sampling Cluster Sampling Confidence Interval Central Limit Theorem Bootstrap Resampling Power Analysis

COMPETING TERMS

Census

RELATIONS

• Narrower: Best-of-N Sampling Cluster Sampling Importance Sampling Negative Sampling Nucleus Sampling Random Sampling Stratified Sampling Systematic Sampling Temperature Sampling Thompson Sampling Top-k Sampling

KNOWLEDGE AREAS

Data Science Mathematics Testing & Validation

TARGET AUDIENCE

Data Scientist Data Analyst BI Specialist Information Analyst Data Steward

EXTERNAL SOURCES

• Wikipedia - Sampling (statistics)
• NIST Engineering Statistics Handbook - Sampling fundamentals and practice guidance
• Penn State STAT ONLINE - Overview of sampling designs
• Khan Academy - Sampling and surveys lessons

TOP QUESTIONS

Why is Sampling essential in data science?

Sampling enables inference without collecting complete data, reducing cost and time. It supports experiments and surveys and underlies most statistical estimates and confidence intervals.

Category: Relevancy

What is the difference between Sampling and a Census?

Sampling studies a subset to infer about a population, while a census measures every unit. Sampling is cheaper and faster, but introduces sampling error that must be managed.

Category: Comparison

How do I choose a Sampling method?

Match methods to objectives and constraints: simple random for unbiased estimates, stratified for subgroup precision, cluster for logistics, and nonprobability only when unavoidable.

Category: HowTo

How large should a Sampling size be?

Use power analysis and desired margin of error to determine size, considering variability and design effect. Larger, well-designed samples reduce uncertainty but increase cost.

Category: Requirements

What are common mistakes in Sampling?

Using convenience samples, ignoring nonresponse, poor frame coverage, and inadequate randomization cause bias. Overlooking design effects can make confidence intervals too narrow.

Category: Mistakes

What is Sampling error versus Sampling bias?

Sampling error is random variability due to observing a subset. Sampling bias is systematic error from a flawed design that skews representativeness and cannot be averaged out.

Category: Misunderstanding

How does stratified Sampling help?

Stratification divides the population into homogeneous groups and samples within each, improving estimate precision and ensuring representation of important subpopulations.

Category: Example

How is Sampling used in A/B testing?

Users are randomly sampled and assigned to variants, ensuring comparability and valid inference of treatment effects while controlling for confounders and reducing selection bias.

Category: Application

What is a hot topic around Sampling today?

Debiasing samples for fairness in ML and using adaptive and active sampling strategies for efficient data collection are active areas of research and practice.

Category: Topic