Which method helps to reduce bias when creating a sample from a population for analysis?

Random sampling minimizes bias by giving each individual in a population an equal chance of being selected. This equal probability helps ensure that the sample is representative of the population, reducing the likelihood that certain characteristics will be over- or under-represented. By relying on randomness rather than subjective criteria or convenience, analysts avoid systematic bias, which can skew results. Random sampling is particularly effective in quantitative research where representativeness is crucial for drawing valid, generalizable conclusions about the broader population. The other options are incorrect because: • Option 1 (small sample size) increases the risk of sampling error and may not capture population diversity. • Option 2 introduces bias based on subjective judgment, compromising sample representativeness. • Option 3 (willing participants) leads to self-selection bias, as the sample may differ from the general population. • Option 5 focuses on subgroups, which is useful for subgroup-specific analysis but limits broader generalization.