Final answer:
Selecting an unbiased sample does not eliminate sampling error; it merely reduces bias. Sampling error occurs due to natural variability between samples and is not completely avoidable simply by using an unbiased sample. Larger sample sizes help reduce sampling error but do not negate it entirely.
Step-by-step explanation:
The statement that sampling error can be eliminated by selecting an unbiased sample is False. An unbiased sample may reduce certain types of error, but it does not guarantee that sampling error is completely eliminated. Sampling error can occur simply because a sample is a subset of the population, and there is variability between samples. No matter how unbiased a sample is, there is always a chance that the sample may not perfectly represent the entire population's characteristics purely due to chance. This sampling variability is distinct from bias, which refers to systematic errors in the sample selection process.
To minimize sampling error, a larger sample is often recommended because it helps to ensure that the sample is more likely to be representative of the population. A biased sampling technique, even with a large sample size, may still not accurately represent the population. Therefore, while an unbiased sample is crucial in reducing sampling bias, a larger sample size helps in reducing the randomness of sampling error.