How can researcher bias impact data interpretation?

Researcher bias can significantly influence how data is interpreted, and option C correctly identifies this impact. When a researcher has preconceived notions or personal biases, it can result in a selective interpretation of the data collected. This means that the researcher might focus more on aspects or findings that align with their biases while disregarding or downplaying information that contradicts those views. This selective lens can distort the overall understanding of the data, leading to conclusions that may not accurately reflect the reality of the studied phenomenon.

The other options do not accurately address the nature of bias in research. Bias does not ensure more accurate data analysis; in fact, it often does the opposite. It also affects data validity, as bias can lead to misinterpretations that undermine the results. Furthermore, bias does not eliminate ethical concerns; rather, it can exacerbate them by compromising the integrity of the research process.