Breaking down the question
The question asks you to explain why random sampling is credited with greater reliability and validity than other methods of drawing a sample. The word "why" signals an explanatory task: you must define the key terms and then set out the logic that connects random selection to these two virtues.
Three concepts must be pinned down. Random sampling is a probability method in which every unit of the population has a known, non-zero and usually equal chance of selection. Reliability refers to the consistency and replicability of results — whether repeated studies yield similar findings. Validity refers to whether the study measures and represents what it claims to, and in the sampling context especially to the generalisability of findings from sample to population.
The research methods in sociology framework provides the background. The heart of the answer is the mechanism: how randomness produces representativeness, controls bias and allows sampling error to be estimated, and therefore underwrites both reliability and validity.
How to approach it
Begin by defining random sampling, reliability and validity, and by noting why sampling is necessary at all — populations are usually too large to study in full. State that the quality of inference from sample to population depends on how the sample is chosen.
Then build the argument in steps: randomness eliminates conscious and unconscious selection bias; it tends to produce a representative cross-section; it permits the calculation of sampling error and confidence limits; and it makes replication meaningful. Explain how each of these bears on reliability and on validity.
Finally, qualify the claim. Note that random sampling secures these virtues only under conditions — an adequate sampling frame, sufficient size, low non-response — and that for some research questions non-random methods are appropriate. Conclude with a balanced verdict.
Model answer
Most social research cannot study an entire population, so it examines a sample and infers conclusions about the whole. The value of those inferences depends entirely on how the sample is selected. Random sampling — a probability method in which every unit has a known and usually equal chance of being chosen — is widely regarded as the soundest basis for such inference precisely because it strengthens both the reliability and the validity of the results.
The first and most important reason concerns the elimination of bias. When a researcher chooses cases by judgement, convenience or accessibility, conscious and unconscious preferences intrude: easy or agreeable respondents are over-represented and awkward ones excluded, distorting the picture. Random selection removes human discretion from the choice of units. Because inclusion is governed by chance alone, no systematic tendency favours particular types of case. The sample is therefore far less likely to be skewed in a hidden direction, which is the principal threat to validity.
Second, randomness tends to produce a representative sample. Over a sufficiently large random draw, the varied characteristics of the population — its distribution of age, class, gender, region and opinion — are reproduced in roughly the same proportions in the sample, because every subgroup has an equal chance of appearing. A representative sample resembles the population in miniature, so findings drawn from it can be generalised to the whole with confidence. Generalisability of this kind is the essence of external validity: the study genuinely tells us about the population it claims to describe, not merely about an unrepresentative fragment.
Third, and distinctively, random sampling allows the researcher to quantify the uncertainty of the estimate. Because selection follows the laws of probability, statisticians can calculate the sampling error and construct confidence intervals — for example, stating that a finding holds within a given margin at a given level of confidence. This is impossible with non-random samples, where there is no principled way to know how far the sample may depart from the population. The ability to measure and report the likely error is a major reason random sampling is trusted: it makes the limits of the finding explicit and testable.
These properties bear directly on reliability, understood as the consistency of results across repetitions. Because a random procedure is systematic and rule-governed rather than arbitrary, another researcher following the same method on the same population should draw a comparably representative sample and reach similar conclusions. Replication becomes meaningful, and consistent replication is the mark of reliable findings. Random methods also lend themselves to the standard tests of statistical significance, which distinguish genuine patterns from those that might arise by chance, further reinforcing confidence in the results.
The claim, however, requires qualification. Random sampling secures these advantages only under favourable conditions. It presupposes an accurate and complete sampling frame — a list of the whole population — which is often unavailable, especially for hidden or marginal groups. The sample must be large enough; small random samples can still be unrepresentative through chance alone. High non-response can reintroduce bias if those who decline differ systematically from those who reply. And randomness guarantees only the absence of selection bias, not the absence of measurement error or poorly designed questions, which threaten validity independently. Moreover, for exploratory, in-depth or qualitative research into rare phenomena, purposive or snowball sampling may be more appropriate than random selection, since the aim there is depth of understanding rather than statistical generalisation.
On balance, random sampling is said to have greater reliability and validity because it removes selection bias, tends to yield a representative cross-section, and uniquely permits the estimation of sampling error and the meaningful replication of results. These strengths make it the benchmark for research that seeks to generalise from a sample to a population. Yet its superiority is conditional on a sound frame, adequate size and low non-response, and it is not the ideal method for every research question. Understood with these qualifications, random sampling remains the most defensible route to trustworthy, generalisable social knowledge.
Examiner's perspective
The frequent shortcoming is to describe how a random sample is drawn without ever explaining why this yields reliability and validity. The examiner rewards the causal logic: randomness removes selection bias, produces representativeness, and allows sampling error to be estimated — and each of these is then tied explicitly to reliability or validity.
Precise use of terms carries weight: reliability as consistency and replicability, validity as generalisability from sample to population, and the distinction between the two. The strongest answers also qualify the claim, noting the need for an adequate sampling frame, sufficient size and low non-response, and recognising that non-random methods suit some research aims. A conclusion that presents random sampling as the conditional benchmark rather than an infallible guarantee marks out a top-band script.