Which of the following best distinguishes random

Solution

Explanation: Random sampling is a probability-based technique where each element of the population has an equal chance of being selected, ensuring that the sample is unbiased and representative. Non-random sampling, on the other hand, involves subjective criteria, such as convenience or judgment sampling, which can introduce bias. Random sampling is widely used for its statistical robustness, while non-random sampling is employed when resources or access to data are limited, but it requires careful evaluation to avoid skewed results. Option A: Random sampling does not guarantee diversity, especially in small samples. Stratified random sampling ensures diversity by design. Option B: Non-random sampling may seem efficient but can lead to inaccurate or biased outcomes, negating computational advantages. Option D: Accuracy depends on how well the sample represents the population, not on the sampling method itself. Random sampling is generally preferred for unbiased results. Option E: Sampling methods, random or non-random, do not eliminate the need for data cleaning, as errors can exist in any dataset.