Model misspecification occurs when a statistical model does not accurately represent the underlying data-generating process. This can lead to incorrect conclusions and predictions, as the model may omit important variables, use the wrong functional form, or assume an inappropriate distribution for the data. In character-based methods, which rely on specific traits or features of the data, model misspecification can particularly affect how well these methods can infer relationships or evolutionary patterns.
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