Final answer:
The answer is True; the assumption behind probability sampling is that with a sufficiently large and random sample, the characteristics of the sample will be representative of the larger population, which is supported by the central limit theorem.
Step-by-step explanation:
True. The basic assumption behind probability sampling is indeed that the sample will reflect the broader population from which it is drawn. This coincides with the central limit theorem, which states that the sampling distribution of the sample means tends to become normal as the sample size increases. This is particularly true with random samples, where each member of the population has an equal chance of being selected.
Random samples are key because they help ensure that the sample represents the various characteristics of the population, such as sex, ethnicity, and socioeconomic level. Despite this effort for accuracy, a sample will never be perfectly representative of the population, resulting in some degree of sampling error. However, as the sample size becomes larger, the sampling error typically becomes smaller, which increases the reliability of the sample data.
Assumptions in probability sampling include the normality of populations, random and independent selection of samples, and the populations having equal standard deviations. When these conditions are met, and the sample is sufficiently large and random, probability sampling can yield a fairly accurate representation of the population.