Final answer:
Snowball sampling is a non-probability technique that does not ensure a statistically representative sample of the overall population, unlike random sampling methods that are designed to represent the population with minimal sampling error, especially with larger sample sizes.
Step-by-step explanation:
The statement is actually false; snowball sampling does not strive to obtain representative samples that statistically represent the overall population. Instead, snowball sampling is a non-probability sampling technique often used in qualitative research where existing study subjects recruit future subjects from among their acquaintances. As such, the resulting sample can be biased and not representative of the entire population.
However, random samples aim to accurately represent the population as a whole. Techniques like simple random sampling, stratified sampling, and others give each individual an equal chance of being included, which increases the likelihood that the sample will be representative of the population. The reliability of such samples is higher when the sample size is large, which is supported by the central limit theorem, stating that the larger the sample, the smaller the sampling error.
In contrast, snowball sampling might be chosen for practical reasons when the population is hard to reach, but the results obtained through this method should not be used to describe the entire population without acknowledging the potential for significant sampling bias.