When using random sampling (based on probability theory):

When using random sampling (based on probability theory):

• A. Results can be generalized from the sample to the population from which the sample was drawn
• B. Results cannot be generalized to any population
• C. Generalization is valid only for very large samples
• D. Generalization is unrelated to sampling method

Answer: (A)

Random sampling uses probability theory to aim for a sample that reflects the population it comes from. Because every member has a known chance of selection, the sample’s characteristics can serve as unbiased estimates of the population’s characteristics, allowing findings to be generalized to that population. This generalization is appropriate for the population from which the sample was drawn, not to any population in general. The method matters—nonrandom approaches undermine external validity, so generalization depends on the sampling procedure being truly random. While larger samples reduce sampling error and improve precision, the ability to generalize rests primarily on how the sample was selected rather than on size alone.

Random sampling uses probability theory to aim for a sample that reflects the population it comes from. Because every member has a known chance of selection, the sample’s characteristics can serve as unbiased estimates of the population’s characteristics, allowing findings to be generalized to that population. This generalization is appropriate for the population from which the sample was drawn, not to any population in general. The method matters—nonrandom approaches undermine external validity, so generalization depends on the sampling procedure being truly random. While larger samples reduce sampling error and improve precision, the ability to generalize rests primarily on how the sample was selected rather than on size alone.