Misinfer

Misinfer is the act or result of drawing a conclusion that is not supported by the available evidence. The term emphasizes the logical error rather than misreading text. It can arise in everyday reasoning, scientific analysis, and artificial intelligence where an inference goes beyond what the data can justify.

Human misinference often involves inferring causation from correlation, overgeneralizing from limited samples, or applying unwarranted priors. In data science and AI, misinference can occur when models learn spurious correlations, suffer from data leakage, or operate under distribution shift, leading to predictions or explanations that do not reflect reality.

Common causes include incomplete data, measurement error, biased samples, model misspecification, and cognitive biases such as confirmation bias. The consequences range from poor decision making to the propagation of misinformation or biased outcomes in automated systems.

Mitigation strategies include explicit checking of assumptions, seeking disconfirming evidence, using robust statistical methods (for example, confidence intervals, cross-validation), and applying principled inference frameworks such as Bayesian reasoning. In AI, techniques include data auditing, counterfactual evaluation, and testing for distribution shifts and model calibration.

See also: inference, logical fallacy, correlation does not imply causation, confirmation bias, data leakage.