Which term describes a systematic error that influences study results?

The correct answer is bias, which refers to a systematic error that consistently distorts study results in a particular direction. This can occur in various ways, such as through the selection of participants, data collection methods, or even in the interpretation of results. Bias can lead to conclusions that do not accurately reflect the true effects or relationships in the data, thus compromising the validity of a study.

For instance, if a study only includes participants from a specific demographic group, the results may not be generalizable to the broader population, which constitutes a selection bias. Understanding and identifying bias is crucial in evidence-based practice because it impacts the reliability of findings and their application in clinical settings.

The other options, while related to research, do not specifically describe systematic errors. Data variance refers to the quantity by which data points differ from each other, and it is a measure of dispersion rather than a systematic error. Randomization is a technique used to eliminate bias by randomly assigning participants to different groups, thereby balancing unmeasured confounding variables. Confounding refers to a situation where the effect of one variable is mixed with the effect of another variable, but it is not a systematic error on its own; rather, it can create misleading associations in study results.