Final answer:
A cluster sample may not serve as a true random sample because it may not accurately represent the entire population, potentially leading to biased results.
Step-by-step explanation:
A cluster sample usually does not qualify as a true random sample because clusters are not representative of the entire population. This means that the technique can lead to results that are biased and may not be representative of the larger population. When using cluster sampling, researchers divide the overall population into clusters based on certain characteristics, and then randomly select individuals from those clusters. Unlike other random sampling methods, such as simple random sampling or stratified sampling, cluster sampling may inadvertently overlook certain segments of the population, especially if the clusters themselves are not representative of the population's diversity.
To increase the representativeness of a cluster sample, researchers might need to ensure that the chosen clusters have a wide range of characteristics that match the broader population. However, this can be challenging and sometimes not feasible, depending on the size and diversity of the population.