Final answer:
The term for when samples differ from their population because of chance factors is called sampling error, which decreases as the sample size increases. This is different from sampling bias, which is a result of non-random sampling methods.
Step-by-step explanation:
When samples differ from their population due to chance factors, it is called sampling error. This is a natural variation that arises when a sample is used to estimate the characteristics of a larger population. To mitigate sampling error, a larger sample size can be used; as the sample size increases, the error decreases.
Sampling error should not be confused with sampling bias, which occurs when some members of the population are less likely to be selected than others, often due to non-random sampling methods. This can lead to incorrect conclusions about the population. Reducing sampling bias is crucial for the validity of the study, which involves ensuring that every member of the population has an equal chance of being included in the sample.
On the other hand, concepts such as population deviation, standard deviation, and sampling variability of a statistic are related but distinct from sampling error and sampling bias. The standard deviation measures the amount of variation or dispersion of a set of values, while sampling variability refers to the differences that may occur in sample statistics from sample to sample. Therefore, the correct answer to the question is A) Sampling error.