ATLASResearch
methods
Quantitative + Mixed methods/ Sampling

Simple random sampling

Give every fixed-size sample an equal chance

Simple random sampling without replacement selects a fixed number of distinct units so that every possible sample of that size has the same probability. A complete frame or an equivalent valid algorithm is essential. Equal inclusion chances apply to the frame, not automatically to everyone in the target population; non-response and missing frame units can still bias the realised sample.

WHEN IT FITS

Use for population description when an adequate sampling frame is available and there is no strong practical reason to stratify or select groups first.

Strengths

  • Selection probabilities and basic estimators are transparent
  • Provides a benchmark for more complex sample designs

Limitations

  • A complete accurate frame may be expensive
  • Dispersed sampled units can raise collection costs

Know the boundary

Random sampling concerns selection from a population; random assignment concerns allocation to conditions.

USED ACROSS
Banking & financeBusiness & MBAPsychologyComputer scienceEducation
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