Which sampling method is likely to produce the least representative sample?

Snowball sampling is often considered likely to produce the least representative sample, primarily due to its reliance on referrals from initial subjects. This method typically begins with a small group of participants who are then asked to recommend others they know. As a result, the sample may become homogenous, as individuals from similar social networks or backgrounds may be more inclined to connect with one another. This can lead to biases in the data and limit the diversity of perspectives that the sample could capture, resulting in less generalizability to the wider population.

In contrast, methods like systematic sampling, stratified sampling, and random sampling are designed to improve representativeness. Systematic sampling involves selecting subjects at regular intervals from a randomly ordered list, which can help ensure a diverse representation. Stratified sampling deliberately divides the population into various subgroups and samples from each, ensuring that different segments are adequately represented. Random sampling relies on random selection, providing each member of the population an equal chance of being included, which further supports representativeness.

Overall, the characteristics of snowball sampling make it more prone to sampling bias and less likely to accurately reflect the broader population compared to the other sampling methods listed.