Felestimat

Felestimat is a term used in measurement science and data analysis to describe a quantified assessment of the error or uncertainty associated with a value, prediction, or model output. It captures how far a reported result may be from the true value and helps distinguish random (stochastic) errors from systematic (bias) errors.

In practice, felestimat is obtained through methods such as error propagation using partial derivatives, Monte Carlo simulation, bootstrapping, or analytical uncertainty propagation. In metrology, frameworks like the Guide to the Expression of Uncertainty in Measurement (GUM) define standard and expanded uncertainties and combine Type A (statistical) and Type B (non-statistical) evaluations to produce a coverage interval with a stated confidence level, typically 95%.

In modeling and statistics, felestimat can refer to predictive error estimates such as mean squared error, mean absolute error, or predictive intervals. In machine learning, approaches include cross-validation error and Bayesian methods that yield credible or posterior intervals to express uncertainty in predictions.

Applications of felestimat span experimental physics, engineering, calibration and instrument performance, sensor networks, and quality control. It supports decision making by expressing confidence in measurements and model results, guiding calibration, and identifying dominant sources of error.

Example: a thermometer reading 23.5 C with a reported standard uncertainty of 0.2 C yields a felestimat indicating the true temperature is likely within a specified interval around the reading, depending on the chosen confidence level.

See also: uncertainty, error analysis, GUM, calibration, confidence interval.