Which bias causes results to be interpreted to support preconceived beliefs?

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Confirmation bias is the tendency to interpret, seek, and remember information in a way that confirms one’s preconceptions. This leads you to focus on results that support your beliefs, give those findings more weight, and downplay or rationalize evidence that contradicts them. For example, a risk analyst who believes a new model is superior will emphasize backtests where the model performed well and overlook or explain away results where it underperformed.

Hindsight bias relates to feeling that an outcome was predictable after it’s known, not to twisting evidence to fit a belief. Regression to the mean is a statistical tendency for extreme results to move toward average on subsequent trials, which can mislead interpretations but isn’t about biasing interpretation to fit beliefs. Selection bias is about how data are gathered or sampled, which can distort conclusions, but the core idea in this question is specifically about interpreting results to reinforce existing beliefs.