Simple random sampling helps avoid selection bias by

Simple random sampling helps avoid selection bias by

• A. Ensuring every member has an equal chance of being selected
• B. Ensuring the sample reflects population subgroups
• C. Selecting participants who volunteer
• D. Using a fixed interval for selection

Answer: (A)

The main idea is that simple random sampling gives every member of the population an equal chance of being chosen, so no one is systematically favored or left out by the selection process. This pure randomness means the sample is not biased by the researchers’ judgments or by patterns in the list of individuals, which helps the sample resemble the whole population. Reflecting population subgroups is the hallmark of stratified sampling, not simple random sampling, so the goal there is about subgroup representation rather than equal chances of selection. Letting participants volunteer creates self-selection bias, since those who volunteer may differ systematically from those who don’t. Using a fixed interval for selection is a systematic approach that follows a rule, which can introduce bias if there’s a hidden pattern in the list.

The main idea is that simple random sampling gives every member of the population an equal chance of being chosen, so no one is systematically favored or left out by the selection process. This pure randomness means the sample is not biased by the researchers’ judgments or by patterns in the list of individuals, which helps the sample resemble the whole population.

Reflecting population subgroups is the hallmark of stratified sampling, not simple random sampling, so the goal there is about subgroup representation rather than equal chances of selection. Letting participants volunteer creates self-selection bias, since those who volunteer may differ systematically from those who don’t. Using a fixed interval for selection is a systematic approach that follows a rule, which can introduce bias if there’s a hidden pattern in the list.