In ACH, how should disconfirming evidence be handled to reduce bias?

• A. Ignore disconfirming evidence.
• B. Disassociate from the disconfirming evidence; keep original weighting.
• C. Increase its weight against the corresponding hypothesis.
• D. Discard the hypothesis entirely after any disconfirming data.

Answer: (C)

The main idea is to update beliefs by treating disconfirming evidence as informative instead of downplaying it, to counteract bias. When data contradicts a favored explanation, you should increase the weight of that disconfirming evidence against the hypothesis. This keeps your assessment honest by reducing overconfidence and forcing you to consider alternative explanations more seriously.

Treating disconfirming evidence as influential helps balance the analysis. It prevents the kind of selective attention that reinforces a initial guess and leads to faulty conclusions. By adjusting the likelihood downward for the hypothesis in light of strong contrary data, you maintain a more accurate view of the situation and stay open to other possibilities.

Ignoring disconfirming data or clinging to the hypothesis despite contrary information both propagate bias. Dismissing the hypothesis after any disconfirming data is too extreme and misses the point of evidence-based updating. Disassociating from the disconfirming data also avoids properly testing the hypothesis. The appropriate approach—weighting the disconfirming evidence more heavily against the hypothesis—promotes objective reasoning and better decision-making.