# Chi Square Goodness Of Fit > Use when you must test whether observed counts fit a stated categorical model: the chi-square goodness-of-fit statistic as the sum of the squared observed-minus-expected gaps over expected counts, the degrees of freedom k minus 1 minus estimated parameters, and the one-tailed p-value from the chi-square survival function via its own regularized lower incomplete gamma, returning the reject or fail-to-reject verdict at a stated significance level. Produces the statistic, degrees of freedom, p-value and verdict that gate model checks such as uniform defect counts per day or Poisson counts per inspection unit. Trigger: chi-square goodness of fit, observed versus expected counts, categorical model fit, count data fit, goodness-of-fit p-value. - Skill: `ashfordeou/chi-square-goodness-of-fit` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add ashfordeou/chi-square-goodness-of-fit` - Raw SKILL.md: https://api.skillmd.com/api/skills/ashfordeou/chi-square-goodness-of-fit/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: Apache-2.0 - Author: ashfordeOU (https://skillmd.com/u/ashfordeou) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ashfordeou/chi-square-goodness-of-fit --- # Chi-square Goodness-of-Fit (cross-cutting/numerics/chi-square-goodness-of-fit) Use when the task is to test whether observed counts in k categories fit a stated categorical model: deciding whether per-day defect counts follow a uniform rate, whether Poisson count data per inspection unit follow a fitted mean, or whether any observed-versus-expected count table is consistent with the model that produced the expected counts. This leaf computes the chi-square goodness-of-fit statistic, sets the degrees of freedom to k - 1 - m (m parameters estimated from the data, 0 by default), evaluates the one-tailed p-value from the chi-square survival function with its own self-contained regularized lower incomplete gamma (series branch for x < a + 1, continued-fraction branch otherwise), and returns the reject or fail-to-reject verdict at a stated significance level. Pure Python stdlib, no sibling imports. It is the one-dimensional categorical model-fit layer: the leaf that owns significance tests for counts arranged in a two-way table is hypothesis-testing, and the leaf that scores parametric continuous distributions from raw floats is probability-distributions; this leaf is the only one that tests user-supplied observed counts against user-supplied expected counts with an exact p-value. ## Domain quick reference - Chi-square goodness-of-fit statistic: stat = sum over the categories of (O_i - E_i)^2 / E_i, with O_i the observed count and E_i the expected count of category i under the stated model. - Degrees of freedom: df = k - 1 - m, where k is the number of categories and m the number of parameters estimated from the data (0 default). Seven categories with no estimated parameters give df = 6. - One-tailed p-value: p = P(chi2_df > stat) = 1 - P(df/2, stat/2), where P is the regularized lower incomplete gamma, series-evaluated when x = stat/2 is below a + 1 = df/2 + 1 and continued-fraction evaluated otherwise so very small survival probabilities keep their precision. - Verdict: reject when p <= alpha, fail-to-reject otherwise, alpha the stated significance level (0.05 default). - Expected-count merging: a category whose expected count falls below 1 folds into the next category, or into the previous one when it is the final category, before the statistic is computed (optional flag). - Small expected counts understate the validity of the chi-square approximation; merging keeps every expected count at or above 1. - Scaling law: multiplying both the observed and the expected counts by one positive constant c multiplies the statistic by exactly c, so the per-category relative gap O_i/E_i is invariant under a common scale change. Spec note: this is the relative-fit scaling law the doubling identity states; a common scale change grows the evidence base, it does not leave the statistic unchanged. - Standards context: NACA-TR-824 frames the numerical-methods background; the relations above are standard engineering methodology, summary-only. ## Workflow 1. Fix the count-data test point: the observed counts per category, the expected counts from the stated categorical model, the significance level alpha (0.05 default), the number of parameters estimated from the data m (0 default), and whether the expected-count traverse must merge categories whose expected count falls below 1. 2. Expected-count traverse: when merging is requested, run merge_small_expected_categories on the observed and expected counts so each deficient category folds into the next category, or into the previous one when it is the final category. 3. Statistic traverse: chi_square_gof_statistic(observed, expected) returns the sum of the squared observed-minus-expected gaps over the expected counts; a perfect model fit gives statistic 0. 4. Degrees-of-freedom bookkeeping: df = k - 1 - m, with k the number of categories after any merging and m the estimated-parameter count; the default m = 0 leaves df = k - 1, and df below 1 raises ValueError. 5. p-value traverse: goodness_of_fit_p_value(statistic, df) returns the one-tailed survival probability of the chi-square law at the statistic, evaluated through the module's own regularized_lower_incomplete_gamma with the series branch for x below a + 1 and the continued-fraction branch otherwise. 6. Verdict bookkeeping: chi_square_goodness_of_fit wraps steps 2 to 5 and returns the fit report dict with keys statistic, df, p_value and verdict; the verdict is reject when p_value is at or below alpha and fail-to-reject otherwise. 7. Uniform-model check: confirm the worked-example anchors below against the spec-prep bounds before quoting a verdict. 8. Verification run: execute python3 scripts/test_chi_square_goodness_ of_fit.py (35 tests, deterministic, offline). ## Worked example Uniform check: total 300 defects over 7 days, expected 300/7 = 42.857 per day, observed [50, 42, 38, 45, 43, 40, 42]. - Statistic traverse: stat = 2.0733 (spec-prep anchor 2.0734 within 0.01), so the per-day relative gaps are mild. - Degrees-of-freedom bookkeeping: df = 7 - 1 - 0 = 6. - p-value traverse: p = 0.91283 (anchor 0.913 within 0.005). - Verdict bookkeeping at alpha 0.05: fail-to-reject, the defect counts are consistent with a uniform daily rate. Skewed set [60, 30, 25, 20, 18, 15, 12], total 180, expected 25.714: - Statistic: 61.8111 (anchor 61.81 within 0.3), df = 6. - p-value: 1.93e-11, below the 1e-9 prep bound. - Verdict at alpha 0.05: reject the uniform model. Two-bin boundary [8, 2] versus [5, 5]: - Statistic: 3.6, df = 1, p = 0.05778 (anchor 0.0578 within 1e-3). - Verdict: fail-to-reject at alpha 0.05, reject at alpha 0.10, the p-value sitting between the two significance levels. Merged example [1, 9, 6] versus [0.5, 10, 5]: - Expected-count traverse folds the first category into the second, giving observed [10, 6] and expected [10.5, 5]; df drops from 2 to 1 and the statistic is 0.2238. ## Verification - Confirm chi_square_gof_statistic([50, 42, 38, 45, 43, 40, 42], [42.857] * 7) returns 2.0733, within 0.01 of 2.0734, and that the skewed set returns 61.8111, within 0.3 of 61.81. - Confirm observed counts equal to the expected counts give statistic 0 and p = 1.0 exactly, and that doubling both the observed and the expected counts doubles the statistic at a fixed relative deviation. - Confirm goodness_of_fit_p_value(3.6, 1) returns 0.05778, matching erfc(sqrt(1.8)) to eight decimals, and that the p-value falls as the statistic grows at fixed df. - Confirm the gamma anchors: P(1, 1) = 1 - 1/e and P(0.5, x) = erf(sqrt(x)) on both the series and the continued-fraction branches. - Confirm chi_square_goodness_of_fit returns exactly the statistic, df, p_value and verdict keys and is deterministic across identical calls. - Confirm ValueError rejection of length mismatches, negative observed counts, non-positive or non-finite expected counts, fewer than two categories, negative or non-finite statistics, degrees of freedom below 1, alpha outside (0, 1) and df below 1 after subtracting estimated parameters. - Run the contract test offline: python3 scripts/test_chi_square_goodness_of_fit.py (35 tests, deterministic). ## Related leaves - cross-cutting/numerics/hypothesis-testing: the leaf that owns significance tests for counts arranged in a two-way table; it does not test observed-versus-expected categorical model fit. - cross-cutting/numerics/probability-distributions: parametric continuous distribution scoring from raw floats, the sibling that handles the continuous fit side this leaf does not cover. - cross-cutting/numerics/fisher-exact-test: exact test for small two-way count tables, an alternative when expected counts are too small for the chi-square approximation. - cross-cutting/numerics/confidence-interval-estimation: the interval view of the same count data, complementary to a reject or fail-to- reject verdict. ## Pitfalls - Quoting the statistic without the degrees of freedom: the p-value traverse needs df = k - 1 - m, and the same statistic at a different df gives a different survival probability, so a naked statistic carries no verdict. - Forgetting the estimated-parameter count: when the expected counts come from a model fit to the data (one Poisson mean estimated from the observed counts), df is k - 2, not k - 1, and the extra degree of freedom inflates the p-value. - Merging after computing the statistic: the expected-count traverse must fold small-expected categories before the statistic traverse, and the degrees of freedom follow the merged category count. - Testing two-way table association here: counts arranged in a table with row and column structure belong to the hypothesis-testing leaf; this leaf tests one-dimensional observed-versus-expected model fit. - Reading the verdict without the significance level: the two-bin example fails to reject at 0.05 yet rejects at 0.10, so every verdict must name its alpha. - Treating a common scale change as neutral: doubling both the observed and the expected counts doubles the statistic, which is the relative- fit scaling law, not an invariance of the statistic itself. ## Behavior contract (gate 3) Run the deterministic contract test (stdlib unittest, offline): python3 scripts/test_chi_square_goodness_of_fit.py The test covers the worked-example anchors (uniform defect counts statistic 2.0733 with p-value 0.91283 and df 6, skewed counts statistic 61.8111 with a survival probability below 1e-9, the two-bin statistic 3.6 with p-value 0.05778), the perfect-fit zero statistic and unit p-value identity, the hand recomputation of the statistic, the doubling and common-scale relative-fit scaling laws, the expected-count traverse and its degrees-of-freedom effect, the estimated-parameter degrees-of-freedom bookkeeping, the gamma closed forms on the series and continued-fraction branches with the branch-boundary continuity, the p-value trend, the reject and fail-to-reject verdicts with the 0.05 to 0.10 boundary, the exact report dict keys, determinism, and every ValueError guard from the validation list. ## Compliance - Standards referenced, not reproduced: NACA-TR-824 is a public-domain NACA technical report framing the numerical-methods context; the chi-square goodness-of-fit relations above are standard engineering methodology, summary-only per standards-map.yaml. - compliance: STANDARDS-REF, gated: false.