feilfordelt

Feilfordelt is a Norwegian term used to describe how errors or faults in measurements, predictions, or models are distributed across observations. It refers to the statistical distribution of the discrepancy between observed values and true or predicted values. The concept is central to error analysis, model diagnostics, and quality control, where understanding the spread and shape of the error distribution informs methodological choices.

In statistical practice, feilfordelt often implies analyzing residuals—the differences between observed data and model estimates. A common assumption in classical methods is that these residuals are unbiased, centered around zero, and have constant variance (homoscedastic) with a roughly normal shape. Deviations from these properties suggest model misspecification, outliers, or varying measurement precision, and may lead to adjustments such as transformation of the data, alternative modeling approaches, or robust estimation techniques.

Common tools for assessing feilfordelt include residual plots to detect patterns, Q-Q plots to judge normality, and statistical tests for normality or heteroscedasticity (for example, tests comparing variance across levels of an explanatory variable). Describing the feilfordelt also helps in choosing appropriate error models, such as normal, Laplace, or heavier-tailed distributions, depending on the context.

Applications of feilfordelt analysis appear across fields like calibration and metrology, regression modeling, quality assurance, and experimental sciences, where reliable uncertainty quantification depends on a proper understanding of how errors are distributed. See also residuals, error distribution, homoscedasticity, and normal distribution.