Goodness-of-fit is a class of statistical measures that quantify the agreement between observed data and a hypothesized model or distribution. It operates by computing a test statistic from sample data and comparing it against a theoretical null distribution to produce a p-value or fit index. Common instances include the chi-squared goodness-of-fit test, Kolmogorov-Smirnov statistic, Anderson-Darling test, and information-criterion measures (AIC, BIC). The concept persists through statistical software implementations, academic literature, and the standardized reporting conventions of scientific practice. [formal: bonus adaptationis | substrate: behavior | horizon: hours | explicit: yes | epoch: 0.01]
Accepted ontology entry
goodness-of-fit
Goodness-of-fit is a class of statistical measures that quantify the agreement between observed data and a hypothesized model or distribution. It operates by computing a test statistic from sample data and comparing it against a theoretica…
Definition
Why it is in scope
A statistical concept that measures how well a model's predictions or theoretical distributions match observed data. It provides quantitative indices (chi-squared, Kolmogorov-Smirnov, AIC, R-squared) for evaluating model adequacy — a human-made evaluation metric built to persist through statistical practice.
Names and aliases
- goodness-of-fiten · CANONICAL
Relations from this entry
- cmrxj3acr03cmsoacx73fal1oDEPENDS_ON →
Goodness-of-fit needs statistical theory to operate now — hypothesis testing, null distributions, test statistics are all statistical. Remove statistical inference and goodness-of-fit procedures cannot function. Present-tense operational dependency per Law 8.
- cmsm0cr9y00331q137lslor2hDEPENDS_ON →
Goodness-of-fit evaluates how well a statistical model fits observed data. Remove the model and the concept stops operating — there is no model to evaluate. The concept's mechanism requires a model as its target. Operational dependency per Law 8.
Relations to this entry
- chi-squared-statistic← SERVES
The chi-squared statistic was designed by Pearson specifically for the purpose of testing goodness-of-fit — measuring how well observed frequencies match expected frequencies under a null model. The test statistic SERVES the goodness-of-fit evaluation: remove goodness-of-fit as a goal and the chi-squared statistic loses its primary designed purpose. Servant (chi-squared-statistic) points at master (goodness-of-fit).
- chi-squared-divergence← SERVES
Chi-squared divergence is constructed as a computationally convenient divergence for comparing distributions, specifically used within goodness-of-fit testing and model comparison. It is built for the sake of evaluating how well a model fits observed data, i.e., it serves goodness-of-fit practice.
Record identity
- Created
- Aug 9, 2026, 1:50 AM UTC
- Content hash
- 71d50d690cd2b1be15895d414dc3bc05f155a9fbd4fc49c1beae9d90c44dc3cb