Sampling and Data Collection
Reliable population conclusions require appropriate measurements and a selection process that limits bias.
Summary
Reliable population conclusions require appropriate measurements and a selection process that limits bias. The population is the complete group of interest; a sample is the subset studied. Data may come from surveys, observations, experiments, or existing records.
Probability sampling supports population estimates with quantifiable sampling uncertainty.
| Sampling method | Selection process | Key feature or risk |
|---|---|---|
| Simple random | Every possible sample of a specified size has an equal chance of selection | Uses chance to select the sample |
| Stratified | Randomly select individuals within every stratum | Ensures subgroup inclusion and can improve precision |
| Cluster | Randomly select natural groups, then study all or some members | Selection begins with groups |
| Systematic | Select at fixed intervals after a random start | Repeating list patterns can distort selection |
The sampling frame is the list or operational source used for selection. Missing population members cause undercoverage. Selection bias favors certain population members; nonresponse bias occurs when respondents and nonrespondents differ in study-relevant ways. Measurement bias arises from instruments, question wording, interview practices, or response behavior.
Random sampling supports generalization to a population. Random assignment places experimental units into treatment groups by chance, reducing confounding and supporting causal inference in a well-designed experiment. Observational studies record characteristics or exposures without assigning treatments.
🧠 Key Concepts
- Population and sample
- Simple random sampling
- Stratified sampling
- Cluster sampling
- Systematic sampling
- Sampling frame
- Selection bias
- Nonresponse bias
- Measurement bias
- Random assignment
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What defines simple random sampling?
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Sampling and Data Collection
📘 Overview Reliable conclusions depend on collecting data that accurately represent the population of interest. Surveys, observations, experiments, and existing records provide data, but each method has distinct risks of error and bias.
🧠 Key Idea A sample supports trustworthy population conclusions when its selection method limits bias and its measurements are appropriate. A larger sample reduces random sampling error but does not automatically correct systematic bias.
⚔️ Core Details: - The population is the complete group of interest; a sample is the subset from which data are collected. - Simple random sampling gives every possible sample of a specified size an equal chance of selection. - Stratified sampling randomly selects individuals within every subgroup, or stratum, of the population. - Cluster sampling randomly selects natural groups and collects data from all or some members of the selected groups. - Systematic sampling selects every fixed-interval member of an ordered list after a random start. - Random assignment places experimental units into treatment groups by chance to reduce confounding.
🎯 Why It Matters: - Probability sampling supports population estimates with quantifiable sampling uncertainty. - Stratification can improve precision and ensure inclusion of small but relevant population subgroups. - List periodicity can distort a systematic sample when its selection interval aligns with a repeating pattern. - Random sampling supports generalization to a population; random assignment supports causal inference in a well-designed experiment.
🧠 Quick Recall: - Sampling frame - the list or operational source from which a sample is selected; missing population members create undercoverage. - Selection bias - systematic distortion caused by a selection process that favors some population members over others. - Nonresponse bias - distortion arising when respondents differ from nonrespondents in ways relevant to the study. - Measurement bias - systematic error caused by instruments, question wording, interview practices, or response behavior. - Observational study - a study that records characteristics or exposures without assigning treatments; associations alone do not establish causation.
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