What are common biases in research design such as selection bias and measurement bias?

What are common biases in research design such as selection bias and measurement bias?

• A. Biases don't matter
• B. Measurement bias is the same as sampling bias
• C. Selection bias occurs when participants are not representative; measurement bias arises from faulty measurement
• D. Selection bias is always good

Answer: (C)

Biases can distort study results, so understanding common types helps you judge research quality. Selection bias happens when the participants in a study aren’t representative of the population you want to learn about. This can occur if you recruit from a specific clinic, rely on volunteers who respond to an ad, or lose participants in a way that’s related to the exposure or outcome. The result is that findings may not generalize beyond the group actually studied. Measurement bias arises from errors in how outcomes or exposures are measured, leading to systematic misclassification. This includes using faulty instruments, inconsistent procedures, or survey questions that push responses in a particular direction. Because the measurement is biased, the observed association can be distorted regardless of who is studied. So the correct idea captures both distinct problems: selection bias concerns who is included, while measurement bias concerns how variables are measured. The other statements either minimize the importance of biases or incorrectly equate different types of bias.

Biases can distort study results, so understanding common types helps you judge research quality. Selection bias happens when the participants in a study aren’t representative of the population you want to learn about. This can occur if you recruit from a specific clinic, rely on volunteers who respond to an ad, or lose participants in a way that’s related to the exposure or outcome. The result is that findings may not generalize beyond the group actually studied.

Measurement bias arises from errors in how outcomes or exposures are measured, leading to systematic misclassification. This includes using faulty instruments, inconsistent procedures, or survey questions that push responses in a particular direction. Because the measurement is biased, the observed association can be distorted regardless of who is studied.

So the correct idea captures both distinct problems: selection bias concerns who is included, while measurement bias concerns how variables are measured. The other statements either minimize the importance of biases or incorrectly equate different types of bias.