Final answer:
The statement is False as larger sample size is often necessary to accurately estimate a population correlation, even if a large correlation is expected. Larger sample sizes reduce sampling errors and increase the representativeness of the population, leading to more precise estimates.
Step-by-step explanation:
The statement, "If we are expecting a certain population correlation to be large, then there would never be any point in sampling more than a smaller number of people when we attempt to estimate it," is False. Even if a large population correlation is expected, it is still important to have a sufficiently large sample size to estimate the correlation accurately.
Regardless of the expected size of the correlation, small sample sizes can lead to increased sampling variability and may not be representative of the population. This can result in inaccurate estimations, reduced power of statistical tests, and a higher chance of Type II errors.
Sampling more people helps to ensure that the sample is more representative of the entire population, which is crucial for generalizing the findings. The Central Limit Theorem suggests that larger samples tend to be more normally distributed and thus more representative.
Moreover, a large sample size can help reduce the sampling error and provide a more precise estimate of the population parameters. As such, sampling should be done with careful consideration of the study objectives and required precision.