What is Random Sampling?
Random sampling is a probability-based method for selecting people from a defined population so each person has a known (usually equal) chance of being chosen. It’s used to produce survey results that can be reasonably generalised to the whole population.
Random sampling means picking participants in a way that does not systematically favour any subgroup — usually by using a list of the population (a sampling frame) and selecting individuals at random. Common approaches include simple random sampling (every individual has the same chance), stratified sampling (populations are divided into subgroups and sampled within each), cluster sampling (randomly selecting groups such as neighbourhoods) and systematic sampling (selecting every nth person from a list). Because selection is probabilistic, random samples reduce selection bias and allow you to estimate how uncertain your survey results are.
Usage example
A council wants a representative view of local residents about a proposed playground. They use the electoral roll as a sampling frame and select 800 households at random. Each household receives the same Hearo survey link; respondents choose their preferred language and reply. The council can then report results that reflect the wider community rather than only people who volunteered.
Practical application
Random sampling matters because it produces more trustworthy, defensible findings for consultations, public opinion work and service evaluations. For organisations working across languages, combining random sampling with Hearo’s multilingual survey delivery lets you reach a representative cross-section of the community while avoiding duplicate forms. If some language groups are small or at risk of being underrepresented, you can use stratified sampling or oversample those groups and apply statistical weights later so their voices are properly heard.
FAQ
How is random sampling different from convenience sampling?
Convenience sampling collects whoever is easiest to reach (e.g., volunteers or visitors to a website) and can produce biased results. Random sampling selects people by chance from a defined population so findings are more likely to reflect the whole population and support generalised conclusions.
Do I need a full list of the population to do random sampling?
A sampling frame (a list or database of the population) makes random sampling straightforward, but there are alternatives when a full list isn't available — for example cluster sampling (randomly selecting geographic areas) or random digit dialing. The method you choose depends on what lists or contact methods you have.
How do I make sure minority language groups are represented?
Use stratified sampling or intentional oversampling: divide the population into language or demographic strata, draw a larger sample from smaller groups, then collect responses in participants’ preferred language. Apply weights in analysis so the final results reflect the real population proportions.
What if many randomly selected people don’t respond?
Non-response reduces the effective sample and can bias results if non-responders differ from responders. Reduce non-response with reminders, easy multilingual surveys (no account required), mixed contact modes, and by tracking response rates by subgroup so you can adjust with weighting or targeted follow-up.