In research, what does "sampling bias" refer to?

Sampling bias refers to a systematic error that occurs when the sample selected for a study is not representative of the larger population from which it is drawn. This lack of representativeness can lead to skewed results and conclusions, as certain groups may be over- or under-represented in the sample.

When researchers rely on biased samples, their findings may not generalize well to the broader population, ultimately impacting the validity and reliability of the study’s outcomes. Recognizing and addressing sampling bias is crucial in the design of research studies to ensure that the findings can be accurately applied beyond the sample studied.

The other options do not accurately define sampling bias. Random selection of participants refers to a method intended to minimize bias and ensure that each member of the population has an equal chance of being selected, which is the opposite of sampling bias. A random distribution of data across all groups does not relate directly to the concept of bias, focusing instead on how data may be organized or presented. A method to ensure demographic diversity could aim to reduce bias, but it does not encapsulate the definition of sampling bias itself.