Which of the following best differentiates random sampling from non-random sampling techniques?

Random sampling is a probability-based technique where every individual in the population has an equal chance of being selected. This ensures the sample is unbiased and representative of the population. It’s widely used in experimental research where statistical accuracy is essential. On the other hand, non-random sampling (such as convenience sampling or judgment sampling) does not guarantee that every individual has an equal chance of being chosen, which can lead to selection bias and less reliable results. Option A is incorrect because random sampling is typically more accurate in representing the population than non-random sampling. Option C is incorrect because non-random sampling does not guarantee that the sample is representative; it often introduces bias. Option D is incorrect as random sampling is used for both small and large populations, depending on the context and feasibility. Option E is incorrect because neither random nor non-random sampling inherently relies on algorithms to select samples; it depends on the method used for selection.