overgeneralized

Overgeneralized is an adjective describing a statement, rule, or belief that is applied too broadly beyond the available evidence, or that ignores relevant exceptions. It can arise when a person generalizes from a subset of cases to a wider category, or when a rule is used in contexts where it does not hold.

In language development, overgeneralization is a common phenomenon as learners acquire productive rules. Children may apply standard grammar rules to irregular forms, producing "goed" instead of "went" or "runned" instead of "ran." Such errors reveal the mind's tendency to systematize language through generalization, often followed by later refinement.

In logic and cognition, overgeneralization denotes a faulty generalization beyond the evidence. It underlies many cognitive biases and stereotypes, such as inferring that "all members of a group share a trait" from a limited observation. Distinguishing between generalization and overgeneralization is central to critical thinking and argument evaluation.

In data science, overgeneralization (or overfitting) occurs when a model learns patterns that do not apply to new data. It results from limited or biased training data and overly complex models. Remedies include regularization, cross-validation, more data, and simpler hypotheses to improve true generalization to unseen cases.

The term is commonly used in education, psychology, linguistics, and statistics to describe broad application of rules or patterns, and to distinguish appropriate generalization from excessive or erroneous broad claims.