Random Sampling

What Is Random Sampling?

Random sampling selects units using a probability mechanism. Each in-scope unit must have a known, positive selection probability. Simple random sampling (SRS) is a particular design with equal probabilities; other probability designs can use unequal probabilities and appropriate weights. This is typically achieved through random selection mechanisms such as random number generators, lottery methods, or computerized selection from a complete sampling frame.

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 estimated sampling uncertainty, subject to coverage, response, measurement, and analysis assumptions. Population sampling is a separate design choice from assigning an intervention.

Why Does Random Sampling Matter?

Probability sampling supports inference to a defined population. A realized sample need not match every population characteristic, and incomplete frames or differential nonresponse can still bias estimates.

Random assignment to treatment supports comparability in expectation within the study sample; it does not guarantee exact realized baseline balance. Random sampling supports population inference. A random population sample with self-selected treatment can remain confounded, even when population estimates are representative.

Choose the selection and assignment designs for the evaluation question. Check any additional deliverables in the applicable funding agreement rather than treating a named sampling method as a universal requirement.

How Is Random Sampling Used 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). Treatment is randomly assigned within the study sample. Population selection may also be random, but this is a separate step. An RCT in a convenience sample can support an internally valid treatment comparison under its assumptions, while generalization beyond that sample remains limited.

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 individual lists are unavailable, a probability cluster sampling design can use area frames and within-cluster listing. LQAS still requires a defensible selection mechanism and is designed for classification. 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.

Method Source

J-PAL, The elements of a randomized evaluation, selection and randomization. This is methodological guidance, not a funding requirement.