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Random sampling is a probability-based technique in which every member of the population has an equal likelihood of being included in the sample. This ensures the sample is representative and that the results can be generalized to the entire population with known levels of accuracy. Random sampling eliminates bias by giving each participant an equal chance, reducing selection biases that can occur in non-random methods. Techniques like simple random sampling or systematic random sampling are common examples of this. Non-random sampling, on the other hand, involves methods where certain individuals may have a higher or lower chance of being selected, which may introduce selection bias. Why Other Options Are Incorrect: • A: Random sampling does not rely on convenience; instead, it aims to randomly select individuals. • B: Non-random sampling does not guarantee equal probability for selection. • D: Sample sizes are not inherently larger in non-random sampling; size is determined by the specific methodology. • E: Both random and non-random sampling can be used for qualitative and quantitative data, depending on the research design.
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