misestimation

Misestimation is the act or result of assessing something inaccurately, especially regarding magnitude, probability, or future outcomes. It refers to judgments, estimates, or model predictions that deviate from what is later observed or accepted as true. The term is widely used in statistics, economics, psychology, and everyday decision making to describe systematic or random errors in assessment.

Types of misestimation include overestimation and underestimation, where a value or likelihood is judged too high or too low. It can also involve estimation bias, sampling error, measurement error, and model misspecification, all of which can produce predictions that diverge from reality. In forecasting, misestimation may arise from incorrect assumptions, missing data, or inappropriate methods, leading to biased or uncertain results.

Causes of misestimation include cognitive biases such as optimism or overconfidence, anchoring to initial values, and the availability heuristic. Data limitations, such as small sample sizes, nonresponse, or poor measurement, as well as methodological issues like inappropriate models or data dredging, can contribute as well. Misestimation can occur at any stage of analysis, from data collection to model selection and interpretation.

Examples include financial forecasts that overstate expected returns, demand predictions that miss peak seasons, or medical risk assessments that misstate the probability of adverse events. The consequences can range from suboptimal business decisions to ineffective public policy or misplaced resources.

Measurement and evaluation of misestimation often rely on comparing estimates with true or later-observed values, using metrics such as error, bias, mean squared error, or calibration. Mitigation strategies include robust data collection, validation and cross-validation, transparency, preregistration, sensitivity analyses, and ensemble methods.

See also miscalculation, bias, estimation error, forecast error.