WorldatWork CCP Compensation Analytics & Insights Practice Test

Random sampling means picking from a population in a way that every possible sample of the desired size has an equal chance of being drawn. This equal-probability principle is what makes the sample representative and allows statistical inferences (like estimating population values or calculating margins of error) to be valid because they depend on known probabilities of selection. A simple way to visualize it is drawing names from a hat or using a random-number generator so that no person or subgroup is favored.

Other described methods are non-random. Selecting participants because they’re easy to reach is convenience sampling and tends to bias results toward those who are readily available. Choosing samples with preferential criteria is purposive or judgmental sampling, which targets specific cases and isn’t random. Limiting samples to a known subgroup without randomization across the whole population also breaks randomness, unless random selection is applied within that subgroup, which isn’t stated in the description.