Quick Answer: The four main random sampling techniques are: (1) Simple random sampling — every member has equal chance, (2) Stratified — population divided into subgroups, sampled proportionally, (3) Cluster — groups are randomly selected, all members surveyed, (4) Systematic — every Nth member selected. Each has ideal use cases and tradeoffs.
1. Simple Random Sampling
Every member of the population has an equal probability of selection. Implemented by assigning sequential numbers and using a random number generator to select. Best for: homogeneous populations where all members are similar. Limitation: requires a complete list of all members (sampling frame), which is often unavailable.
2. Stratified Random Sampling
The population is divided into subgroups (strata) based on a shared characteristic (age, gender, region). Members are randomly selected from each stratum proportionally. This guarantees that key subgroups are represented in the sample — even if they are small. Best for: heterogeneous populations where key subgroups must be represented.
3. Cluster Sampling
The population is divided into groups (clusters), then some clusters are randomly selected and all members within selected clusters are surveyed. Best for: geographically dispersed populations where surveying every individual would require extensive travel. Less precise than stratified but more practical for large geographic studies.
4. Systematic Sampling
A random starting point is chosen, then every Nth member of the list is selected (e.g., every 10th person). Simple to implement without a random number generator for each selection. Risk: if the list has a periodic pattern aligned with the interval, results may be biased.
| Method | Ideal For | Requires Full List? | Bias Risk |
|---|---|---|---|
| Simple Random | Homogeneous populations | Yes | Low |
| Stratified | Diverse populations, key subgroups | Yes (per stratum) | Very low |
| Cluster | Geographic surveys | No (just clusters) | Medium |
| Systematic | Sequential lists | Yes | Low-medium |