Definition
Random sampling (also called simple random sampling or SRS) is a probability sampling method where every member of the target population has an equal, known, non-zero chance of being selected for the sample. This is typically achieved through random selection mechanisms such as random number generators, lottery methods, or computerized selection from a complete sampling frame.\n Unlike non-probability methods like purposive sampling, random sampling enables statistical inference, allowing researchers to calculate sampling error, construct confidence intervals, and generalize findings from the sample to the broader population with known precision. This makes it the gold standard for impact evaluations and quasi-experimental designs that require causal attribution.
Why It Matters
Random sampling is foundational to quantitative M&E work because it directly addresses selection bias, one of the most common threats to internal validity. When participants are selected randomly rather than through convenience or researcher discretion, the resulting sample is statistically representative of the population, not because it perfectly mirrors every characteristic, but because selection is independent of participant attributes.
This representativeness enables two critical capabilities: first, calculating the margin of error and confidence intervals that quantify result precision; second, supporting causal claims by ensuring treatment and control groups (in experimental designs) are comparable at baseline. Without random sampling, observed outcomes may reflect pre-existing differences rather than programme effects.
For donors requiring evidence of causal impact, particularly USAID, DFID, and the World Bank, random sampling is often a prerequisite for evaluation designs that support attribution claims.
In Practice
Random sampling appears in several common M&E contexts:
Baseline surveys for impact evaluations. A complete sampling frame (list of all population members) is obtained, then a random subset is selected for participation. For example, selecting 500 households from a registry of 5,000 eligible households using a random number generator.
Randomized controlled trials (RCTs). After random sampling identifies eligible participants, they are randomly assigned to treatment or control groups, a two-stage randomization process that maximizes internal validity.
Quality assurance sampling. Programs randomly select service records or beneficiary files to verify data quality and compliance with protocols.
Challenges in implementation: Random sampling requires a complete, up-to-date sampling frame, which may not exist in many field contexts. When frames are incomplete, cluster sampling or LQAS may be more practical. Additionally, random sampling can be logistically expensive if the population is geographically dispersed.
Alternatives: When random sampling is infeasible, purposive sampling offers a non-probability alternative for qualitative depth, though without generalizability. Cluster sampling maintains probability methods while reducing costs when frames exist only at group level.
Related Topics
- Sampling Methods, Overview of probability and non-probability approaches
- Quasi-Experimental Design, Impact evaluation methods where random sampling may be constrained
- Data Quality Assurance, Random sampling for verification activities
- Quantitative Data, Data type most compatible with random sampling