Certified Professional in Learning and Performance (CPLP) Practice Exam 2026 - Free CPLP Practice Questions and Study Guide

The correct answer is random sampling. In random sampling, each individual in the population has an equal chance of being selected to participate in the study. This method helps to minimize bias and ensures that the sample represents the larger population accurately.

Random sampling is essential in statistical analysis because it allows researchers to make generalizations about the population based on the sample data. This equal probability of selection is a key feature that distinguishes random sampling from other techniques, such as stratified random sampling, which involves dividing the population into strata and then performing random sampling within each stratum. While stratified sampling reduces variance within the sample, it does not provide each individual across the entire population an equal chance of selection, as certain strata may have different probabilities based on their structure.

Random selection, while similar in name to random sampling, is often more broadly defined to include any process of choosing individuals without bias. However, it does not specifically guarantee that every individual has the same probability of being selected across the entire population like random sampling does. Random assignment refers to how participants are assigned to different groups in an experiment, rather than how participants are selected from the population, making it irrelevant in this context.