Final answer:
Random sampling gets more difficult without comprehensive lists because it can create a biased sample that is not representative of the entire population, leading to reduced generalizability of research findings.
Step-by-step explanation:
Random sampling is a crucial technique in statistics that involves selecting a representative subset from a larger population so that every individual has an equal chance of being chosen. The difficulty of performing random sampling increases when comprehensive lists are not available because obtaining a truly random and representative sample becomes more challenging. Without a complete list of the population, some individuals might have a lower chance of being selected, resulting in a sample that may not accurately reflect the population's characteristics. This lack of accessibility can lead to a bias in the sample, making it less representative and potentially affecting the generalizability of findings.
The absence of comprehensive lists can:
- Lead to biased samples as it might systematically exclude certain members of the population.
- Increase the risk of non-response bias if certain groups are less likely to participate due to the sampling method.
- Reduce the representativeness of the sample by not covering the entire population.
- Hinder the generalizability of findings because the sample does not accurately represent the whole population.
In summary, without comprehensive lists, researchers struggle to ensure that random sampling techniques are used correctly, which is vital for minimizing bias and error. Hence, when such lists are not available, the efficacy of the random sampling process can be compromised, negatively impacting the research's validity and reliability.