Final answer:
Surveys with larger sample sizes may introduce biases such as self-selection bias, and past a certain point, increases in sample size do not significantly enhance accuracy and are not cost-effective. A survey's representativeness is crucial for accurate generalizations about the whole population, not just its size.
Step-by-step explanation:
The assertion that “When it comes to surveys, bigger isn't always better” points to the fact that larger surveys may not always provide more accurate or better data. A critical issue with large surveys is potential bias. For instance, large-scale internet surveys may suffer from self-selection bias as respondents opt in or out, potentially skewing results compared to the actual population. Despite having many participants, such surveys might not accurately reflect the diversity of views if they are not representative.
Another aspect is diminishing returns on precision past a certain point. For example, while Gallup uses around five hundred respondents, other organizations opt for larger samples. However, increasing the sample size beyond what is needed to be representative only marginally increases accuracy and isn't cost-effective. This principle highlights the importance of quality over quantity in sampling.
To illustrate, imagine a survey on dietary habits that exclusively samples gym-goers. Despite having thousands of responses, if the purpose is to generalize findings to the entire population, this survey fails due to its non-representative nature. It over-represents health-conscious individuals and under-represents others, resulting in skewed data. This example underscores the necessity of an appropriate sample that accurately reflects the population's diversity, over merely pursuing larger sample sizes.