Which bias occurs when researchers interpret data in a way that supports their hypothesis?

The phenomenon in question is known as confirmation bias. This cognitive bias occurs when researchers favor information that confirms their preconceived notions or hypotheses while disregarding evidence that contradicts them. In the research context, when a scientist interprets data through the lens of their expectations, they may unintentionally emphasize findings that support their hypothesis and overlook or minimize those that do not.

Confirmation bias can lead to skewed research outcomes, as the interpretation of data becomes selective. Researchers might consciously or unconsciously design their studies, analyze their results, and report their findings in a manner that reinforces their initial beliefs. This bias is particularly concerning because it can compromise the validity of research conclusions and potentially mislead future studies.

In contrast, experimenter bias involves the researcher's influence on the participants or the outcome of an experiment due to their expectations. Sampling bias refers to flaws in sampling that result in a non-representative selection of participants, which can affect the generalizability of findings. Survivorship bias occurs when analyses focus on the subjects that survived a process and neglect those that did not, leading to potentially misleading conclusions. Each of these biases affects research integrity in different ways, but confirmation bias specifically relates to interpreting data in a way that aligns with established hypotheses.