Final answer:
Random sampling ensures each individual has an equal chance of being selected, making survey results representative. The size of a random sample and the use of proper sampling methods affect the quality of data. Once a representative sample size is reached, further increases yield minor additional accuracy.
Step-by-step explanation:
Contrary to the belief that surveying a large number of people guarantees representativeness, it is the random sampling method in surveys that ensures each individual in a population has an equal chance of being included. Therefore, the representativeness of survey results is not solely dependent on a large sample size, but rather on whether the sample is a random sample of the population.
When researchers use random sampling methods like simple random sampling, stratified sampling, cluster sampling, or systematic sampling, they can draw conclusions that are more likely to represent the broader population. This is because these methods reduce the chances of bias that can arise from nonrandom methods such as convenience sampling. It's important to note that even with a random sample, results can vary, but larger, well-chosen samples tend to model the population more accurately.
Nonetheless, once a sample reaches a certain threshold of size and the results become representative, further increasing the sample size yields only minor increases in accuracy relative to the additional costs. As such, many polling organizations find a balance between sample size and cost efficiency to produce reliable data on public opinion or other research areas.