{"id":166,"date":"2026-10-13T12:06:19","date_gmt":"2026-10-13T12:06:19","guid":{"rendered":"https:\/\/therisk.global\/health-nexus\/?page_id=166"},"modified":"2026-10-13T19:16:41","modified_gmt":"2026-10-13T19:16:41","slug":"probability-statistics","status":"publish","type":"page","link":"https:\/\/therisk.global\/health-nexus\/stem\/probability-statistics\/","title":{"rendered":"Statistics for health evidence"},"content":{"rendered":"\n<!-- Nexus network page \u00b7 https:\/\/therisk.global\/health-nexus\/stem\/probability-statistics\/ \u00b7 source template \u00b7 generated by integrations\/wordpress\/network\/build.mjs, never edit this file --><script type=\"text\/x-nexus\" class=\"_iub_cs_skip\" id=\"nxe-fig-engine\" 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auto;padding:0 16px;font-family:ui-sans-serif,system-ui,-apple-system,\"Segoe UI\",sans-serif;line-height:1.55;overflow-wrap:break-word}#nxe-n27xejc8.nxe :where(*,*::before,*::after){box-sizing:inherit}#nxe-n27xejc8.nxe :where(section,header,footer,nav,aside){display:block;margin:0 0 32px}#nxe-n27xejc8.nxe :where(h1,h2,h3,h4){line-height:1.2;margin:24px 0 12px;font-weight:600}#nxe-n27xejc8.nxe :where(p,ul,ol,dl,table,figure,details){margin:0 0 16px}#nxe-n27xejc8.nxe :where(ul,ol){padding-left:20px}#nxe-n27xejc8.nxe a{color:inherit;text-decoration:underline;text-underline-offset:2px}#nxe-n27xejc8.nxe table{border-collapse:collapse;width:100%;display:block;overflow-x:auto}#nxe-n27xejc8.nxe :where(th,td){padding:6px 8px;text-align:left;vertical-align:top;border-bottom:1px solid;border-color:inherit}#nxe-n27xejc8.nxe svg{display:none}#nxe-n27xejc8.nxe svg.nx-i{display:inline-block;width:18px;height:18px;vertical-align:-3px}#nxe-n27xejc8.nxe :where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n27xejc8.nxe img{max-width:100%;height:auto}<\/style><svg class=\"nx-defs\" aria-hidden=\"true\" focusable=\"false\" width=\"0\" height=\"0\"><defs><linearGradient id=\"nx-gi\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-giu\" gradientUnits=\"userSpaceOnUse\" x1=\"0\" y1=\"0\" x2=\"800\" y2=\"0\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-gp\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gp-a\"\/><stop offset=\"1\" class=\"nx-gp-b\"\/><\/linearGradient><\/defs><\/svg><section class=\"nx-hero\" data-layout=\"protocol\" aria-labelledby=\"nx-title-1\"><div class=\"nx-container\"><div class=\"nx-theme-toggle\" role=\"group\" aria-label=\"Appearance of this page\" hidden><button type=\"button\" data-mode=\"light\" aria-pressed=\"false\">Light<\/button><button type=\"button\" data-mode=\"dark\" aria-pressed=\"false\">Dark<\/button><button type=\"button\" data-mode=\"auto\" aria-pressed=\"true\">Auto<\/button><\/div><div class=\"nx-folio\" aria-label=\"Document reference\"><strong>G04 \u00b7 METHOD \u00b7 probability-statistics<\/strong>GCRI dossier \u00b7 service level R0<\/div><div class=\"nx-hero-grid\"><div class=\"nx-hero-text\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">GCRI<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Health Nexus<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Established<\/span><\/div><div class=\"nx-accent-row\"><span class=\"nx-accent-rule\" aria-hidden=\"true\"><\/span><span>Method page \u00b7 Probability and statistics<\/span><\/div><h1 id=\"nx-title-1\" class=\"nx-hero-title\">Statistics for health evidence<\/h1><p class=\"nx-tagline\">Probability and statistics turn incomplete, delayed and biased health data into estimates with stated uncertainty. In this field they handle survey sampling, small numbers, under-reporting and shifting baselines, so that every indicator carries a range and the assumptions behind it rather than a single, overconfident number.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/therisk.global\/health-nexus\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/therisk.global\/health-nexus\/stem\/\">Back to STEM foundation of Health Nexus<\/a><\/div><\/div><div class=\"nx-hero-panel\"><div class=\"nx-lv-steps\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Protocol<\/strong><\/span><span class=\"nx-eyebrow\">6 steps<\/span><\/div><ol><li><a href=\"#nx-sec-brief-answer\"><span class=\"nx-lv-steps-n\">01<\/span><span class=\"nx-lv-steps-t\">In brief<\/span><span class=\"nx-lv-steps-d\">The short answer<\/span><\/a><\/li><li><a href=\"#nx-sec-application\"><span class=\"nx-lv-steps-n\">02<\/span><span class=\"nx-lv-steps-t\">Application<\/span><span class=\"nx-lv-steps-d\">What probability and statistics does here<\/span><\/a><\/li><li><a href=\"#nx-sec-terms\"><span class=\"nx-lv-steps-n\">03<\/span><span class=\"nx-lv-steps-t\">Key terms<\/span><span class=\"nx-lv-steps-d\">Terms used on this page<\/span><\/a><\/li><li><a href=\"#nx-sec-canvas\"><span class=\"nx-lv-steps-n\">04<\/span><span class=\"nx-lv-steps-t\">Canvas<\/span><span class=\"nx-lv-steps-d\">The method as a run graph<\/span><\/a><\/li><li><a href=\"#nx-sec-data\"><span class=\"nx-lv-steps-n\">05<\/span><span class=\"nx-lv-steps-t\">Data<\/span><span class=\"nx-lv-steps-d\">What the method consumes<\/span><\/a><\/li><li><a href=\"#nx-sec-checks\"><span class=\"nx-lv-steps-n\">06<\/span><span class=\"nx-lv-steps-t\">Validation<\/span><span class=\"nx-lv-steps-d\">How results are checked<\/span><\/a><\/li><\/ol><p class=\"nx-register-foot\">GCRI \u00b7 Established \u00b7 service level R0<\/p><\/div><\/div><\/div><\/div><\/section> <nav class=\"nx-index-strip\" data-nx-spy=\"true\" aria-label=\"On this page\"><div class=\"nx-container\"><a href=\"#nx-sec-brief-answer\"><span>01<\/span>The short answer<\/a><a href=\"#nx-s-3-sec\"><span>02<\/span>Tail fit, in outline<\/a><a href=\"#nx-sec-application\"><span>03<\/span>What probability and statistics does\u2026<\/a><a href=\"#nx-sec-terms\"><span>04<\/span>Terms used on this page<\/a><a href=\"#nx-sec-field\"><span>05<\/span>Access to care in Indonesia<\/a><a href=\"#nx-sec-canvas\"><span>06<\/span>The method as a run graph<\/a><a href=\"#nx-sec-data\"><span>07<\/span>What the method consumes<\/a><a href=\"#nx-sec-checks\"><span>08<\/span>How results are checked<\/a><\/div><\/nav> <section id=\"nx-sec-brief-answer\" class=\"nx-section\" data-hue=\"health\" data-variant=\"split\" aria-labelledby=\"nx-s-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">01<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">In brief<\/span><\/div><h2 id=\"nx-s-2\" class=\"nx-section-title\">The short answer.<\/h2><p class=\"nx-lede\">Health statistics is the application of probability and statistics to incomplete, delayed and biased health data so that estimates carry stated uncertainty. It handles survey sampling, small numbers, under-reporting and shifting baselines, such as the expected deaths against which excess mortality is measured.<\/p><\/div><\/div><\/section><section id=\"nx-s-3-sec\" class=\"nx-section\" data-hue=\"health\" data-tone=\"alt\" aria-labelledby=\"nx-s-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">02<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Console<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">Tail fit, in outline.<\/h2><p class=\"nx-lede\">The working screen behind this method: its structure, terms and states. Names are placeholders and values illustrative.<\/p><\/div><div class=\"nx-section-body\"><figure class=\"nx-fk nx-cx\" data-fk=\"cx-health-method-tail\" data-cx=\"health.method-tail\" data-variant=\"section\" aria-label=\"Tail fit: illustrative console with placeholder names and values\"><div class=\"nx-fk-head\"><div class=\"nx-fk-heading\"><h3 class=\"nx-fk-title\">Tail fit<\/h3><p class=\"nx-fk-sub\">Health \u00b7 Statistics for health evidence \u00b7 Run R-6555<\/p><\/div><div class=\"nx-fk-actions\"><button type=\"button\" class=\"nx-fk-btn\" data-fk-act=\"menu\" aria-haspopup=\"true\" aria-expanded=\"false\" hidden><svg viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M12 4v11\"\/><path d=\"m7 10 5 5 5-5\"\/><path d=\"M5 20h14\"\/><\/svg>Export<\/button><ul class=\"nx-fk-menu\" data-fk-menu=\"true\" hidden><li><button type=\"button\" data-fk-act=\"export-png\">Image<small>PNG of the map or chart<\/small><\/button><\/li><li><button type=\"button\" data-fk-act=\"export-csv\">Data<small>CSV of the figure's values<\/small><\/button><\/li><\/ul><\/div><\/div><div class=\"nx-cx-status\"><span class=\"nx-fk-pill\" data-state=\"near\"><i aria-hidden=\"true\"><\/i>Shape parameter uncertain<\/span><span class=\"nx-cx-prod\">GCRI \u00b7 Health Nexus<\/span><\/div><div class=\"nx-cx-grid\"><div class=\"nx-fk-panel nx-cx-p\" data-w=\"4\" data-t=\"chart\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Return level curve<\/h4><\/div><\/div><svg class=\"nx-fk-svg\" viewBox=\"0 0 460 210\" width=\"460\" height=\"210\" role=\"img\" aria-label=\"Return level curve\" focusable=\"false\" tabindex=\"0\" preserveAspectRatio=\"xMidYMid meet\" data-fk-chart=\"cx-health-method-tail-c0\" data-fk-xs=\"log,2,500,36,446\" data-fk-ys=\"lin,0,100,180,20\" data-fk-xf=\"rp\" data-fk-series=\"OEP|ink|l||2:3.3;5:9.5;10:16.9;20:25.7;50:35.8;100:47.1;200:59.7;500:73.6~AEP|acc|l||2:3.5;5:10.1;10:18.2;20:27.9;50:39.3;100:52.3;200:67;500:83.5\" data-nx-z=\"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\"><\/svg><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"2\" data-t=\"kv\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Method card<\/h4><\/div><\/div><dl class=\"nx-fk-dl\"><div><dt>Method<\/dt><dd>Probability and statistics<\/dd><\/div><div><dt>Approach<\/dt><dd>peaks over threshold<\/dd><\/div><div><dt>Threshold<\/dt><dd>97th percentile<\/dd><\/div><div><dt>Label<\/dt><dd><span class=\"nx-fk-pill\" data-state=\"info\"><i aria-hidden=\"true\"><\/i>MODELLED<\/span><\/dd><\/div><\/dl><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"6\" data-t=\"table\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Parameters<\/h4><p class=\"nx-fk-panel-sub\">generalised Pareto fit<\/p><\/div><\/div><div class=\"nx-fk-tablewrap\" data-fk-view=\"table:cx-health-method-tail-t2\"><table class=\"nx-fk-table\" data-fk-table=\"cx-health-method-tail-t2\" aria-label=\"Parameters\"><thead><tr><th scope=\"col\" data-col=\"c0\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c0\">Parameter<\/button><\/th><th scope=\"col\" data-col=\"c1\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c1\">Estimate<\/button><\/th><th scope=\"col\" data-col=\"c2\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c2\">Lower<\/button><\/th><th scope=\"col\" data-col=\"c3\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c3\">Upper<\/button><\/th><th scope=\"col\" data-col=\"c4\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c4\">Note<\/button><\/th><\/tr><\/thead><tbody><tr data-fk-key=\"cx-health-method-tail:r2-0\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Shape \u03be<\/td><td data-num=\"true\" data-sv=\"0.28\">0.28<\/td><td data-num=\"true\" data-sv=\"-0.04\">\u22120.04<\/td><td data-num=\"true\" data-sv=\"0.48\">0.48<\/td><td>heavy tail if above zero<\/td><\/tr><tr data-fk-key=\"cx-health-method-tail:r2-1\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Scale \u03c3<\/td><td data-num=\"true\" data-sv=\"2.3\">2.3<\/td><td data-num=\"true\" data-sv=\"1\">1.0<\/td><td data-num=\"true\" data-sv=\"7.5\">7.5<\/td><td>units of the variable<\/td><\/tr><tr data-fk-key=\"cx-health-method-tail:r2-2\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Exceedances<\/td><td data-num=\"true\" data-sv=\"136\">136<\/td><td data-num=\"true\"><\/td><td data-num=\"true\"><\/td><td>declustered<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><div class=\"nx-fk-tip\" aria-hidden=\"true\" hidden><\/div><figcaption class=\"nx-fk-cap\"><span>Tail fit: the screen's structure, terms and states for Health.<\/span><span data-honesty=\"true\">Placeholder names and illustrative values; a design, not a record or a live system.<\/span><\/figcaption><\/figure><\/div><\/div><\/section><section id=\"nx-sec-application\" class=\"nx-section\" data-hue=\"health\" aria-labelledby=\"nx-s-4\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">03<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Application<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">What probability and statistics does here.<\/h2><p class=\"nx-lede\">Probability and statistics provide the mathematical framework for describing uncertainty and learning from data. They underpin estimation, testing and prediction in every risk discipline. In health and biosecurity, statistical methods quantify the likelihood and uncertainty of losses across interconnected systems. They make the evidence behind risk decisions explicit and reproducible. The canonical description is on <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/probability-statistics\/\">the STEM Commons<\/a>.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cols\"><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M21.21 15.89A10 10 0 1 1 8 2.83\"\/><path d=\"M22 12A10 10 0 0 0 12 2v10z\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Survey-weighted and small-area estimation<\/h3><p class=\"nx-p nx-mt-1\">Household survey data are analysed with their sampling design, and small-area models borrow strength across districts to estimate subnational coverage, with credible intervals mapped beside the estimates.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 3v18h18\"\/><path d=\"m19 9-5 5-4-4-3 3\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Expected baselines for excess mortality<\/h3><p class=\"nx-p nx-mt-1\">Models fitted to past mortality with trend and seasonality give the expected count against which observed deaths are compared, and the baseline's uncertainty is carried into the excess.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><circle cx=\"12\" cy=\"12\" r=\"10\"\/><circle cx=\"12\" cy=\"12\" r=\"6\"\/><circle cx=\"12\" cy=\"12\" r=\"2\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Correcting for under-ascertainment<\/h3><p class=\"nx-p nx-mt-1\">Capture-recapture, serosurveys and multiplier methods estimate how many infections surveillance misses, so case counts are read against what they are likely to represent.<\/p><\/article><\/div><\/div><\/div><\/section><section id=\"nx-sec-terms\" class=\"nx-section\" data-hue=\"health\" data-tone=\"alt\" data-variant=\"stack\" aria-labelledby=\"nx-s-5\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">04<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Key terms<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">Terms used on this page.<\/h2><p class=\"nx-lede\">Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Term<\/th><th scope=\"col\">Definition<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Term\">Excess mortality<\/td><td data-label=\"Definition\">Deaths above the number expected from a baseline, estimated with a model whose assumptions are stated.<\/td><\/tr><tr><td data-label=\"Term\">Under-ascertainment<\/td><td data-label=\"Definition\">The gap between true infections or cases and those detected by surveillance.<\/td><\/tr><tr><td data-label=\"Term\">Survey weight<\/td><td data-label=\"Definition\">A factor applied to each survey response so the sample represents the population.<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-field\" class=\"nx-section\" data-hue=\"health\" aria-labelledby=\"nx-s-6\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">05<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Geography<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">Access to care in Indonesia.<\/h2><p class=\"nx-lede\">How far each division sits from a large city, with the roads and airports that carry patients and supplies, shown for Indonesia as an example geography for this subject.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><div class=\"nx-map nx-cmap\" data-nx-map=\"ctx\" data-cmap=\"indonesia:health\" data-ramp=\"red\" data-basis=\"case\" data-focus=\"0 0 2000 1000|1646 420 1928 782|834 221 1040 487|715 364 914 535\"><figure class=\"nx-fk\" data-fk=\"map-indonesia-health\" data-ramp=\"red\" data-nx-z=\"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\"><figcaption class=\"nx-fk-cap\"><span>How far each division sits from a large city, with the roads and airports that carry patients and supplies.<\/span><span data-honesty=\"true\">Indicator derived from Natural Earth at map scale; a screening view, not an assessment.<\/span><\/figcaption><\/figure><div class=\"nx-live-slot nx-live-slot--map\" data-nxf-slot=\"map\" data-nxf-kind=\"map\" data-nxf-resource=\"nexus:\/\/gcri\/G04\/stem\/probability-statistics\/#map\" data-nxf-title=\"Health Nexus live map\" data-nxf-height=\"560\" hidden><\/div><div class=\"nx-map-hud\"><span class=\"nx-eyebrow\">GCRI \u00b7 Health Nexus \u00b7 field<\/span><span class=\"nx-eyebrow nx-map-hud-right\">DESIGN FIGURE<\/span><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-canvas\" class=\"nx-section\" data-hue=\"health\" data-tone=\"alt\" aria-labelledby=\"nx-s-7\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">06<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Canvas<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">The method as a run graph.<\/h2><p class=\"nx-lede\">Data from the source register feeds the methods; calibration and an independent test gate the estimate and its limits; the decision stays with the competent authority.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-plate\" data-wide=\"true\"><div class=\"nx-plate-label\"><span class=\"nx-eyebrow\"><strong>Method canvas<\/strong> \u00b7 Probability and statistics as applied here<\/span><span class=\"nx-eyebrow\">Design, not a record<\/span><\/div><div class=\"nx-figure-frame\" data-scene=\"true\"><svg class=\"nx-figure nx-ui nx-dag\" viewBox=\"0 0 1200 564\" data-nx-dag=\"true\" data-w=\"1200\" data-h=\"564\" role=\"img\" aria-labelledby=\"nx-dag-g04-stem-probability-statistics-catmodel-t nx-dag-g04-stem-probability-statistics-catmodel-d\" data-nx-z=\"zV3NcuO4EX4VFHPJVoY08cO\/qpGrvLZ21xVbdvyzm8mNlmCJtRSpkJTHnlOOOech8gq551HyJCn8kAQlUQJF2ruHnQHHZqO70f2h8QHEfp7R5\/z08yosCpolIJqNjOTVnIVzc24TMy\/o0lxl6VP4FMVR8WbmRVhEeRFNc3MaFst0RmNzlha5Ab5Gs2IxMqBrgAWN5otCtKXgxyQq8pGxzml2vwqn9CZ5zKlx+nkaZdOYgmkc5uzHTNZzROOZAaavI8M3wPSN\/5WNDNvyjJPTzydS4unnZZj9SrOjVA6zLP1qgJeIfv0+fR0ZNrCBD1g\/9PmvI8PjjS8jgxhA9PKLsM4tn3+SJroGSLOIJsXICNdFyjrLCjOjLzTj9q3CYlFbJ7udjYxrwLq8Aj4g4Ip3\/o0bJ6T3t82Moxf6Gxko+9aw8kQEX0anRS1jGiYvoRJQyLbrkHJcYoDslel1Il9s\/73nKI5HxjqL\/\/iHY2L6u6qLSrdVmNDYZHINwLxqgDf+504dXJsJiKNEie9FGGVmboBXyN97ReVLb5C\/AN5Q+WJBX5Wel2mSgrUZJb8i3jX0ed\/YMcBzmhRmHn2jIwPalmMA9qYZJtNFmo0MPmAGiCnLGjNfhdMombNssgldGqfZOsnBCVjQMC4WZkJf1+xxGhYmdwQ4AaqrwmQm3fX5hPWy6Z8knVGzCOdgbRZP5lM4m1OuLbGh5XKFEay8FSAFKnwxrLjV8lK0yX4qhBJPCsXYavrB33LDMprNYrrLD5C54WJ8f\/njBNw9TkrD5rUGxZMBZmERmuG0iNJkZHxL06UZJQbI0piOjKd1UaSJAYrwKUpmVARGmEWhGYdPNBa\/D6LE2Ar1RbTipgS+ZxHhnzqaELEUMGU\/YB4ih2MjCALLE44hh8Kj3S+2yxzzp9IfJ3MNn6TroptT2AvtXnGI5Q\/nFdez4EBe+d8\/\/6Xrl+dI3yXsd9u9YQebvsAe888xvkCeFQzkix8uH3R9sYrDN21n8F9u9YbvuCWi1P4ghGXREf7wPV9GWn9\/3F6dfVEd0mKA52yFdmAfGdq+jS08kPpXZ19uHh\/Af\/8Dru52g\/w8S9fCBiTyyQvqiMSOBZUxgFDO1\/4+KzCXhoW0ADdMCHTnM4iY+j+d\/e3s7kJTcejjOnYYaELF\/eWUpK85DLw+qo\/\/entz\/3g3BmeTC3Bxdn324\/iQHQ4kXOn9g4AR0jbFQZXEfgPxw+XkbHJ+eXZ1yAafYMt3D9nQZTh8p5LYz4br8cPd5fn9IQugbfsWEQmIfK8uBz3bcrzjkgHasJKJbdjHiu8fv4yrVG7W63Q2p0D8VVbtDK\/zgq742oA\/PWfpcmRMw0I+F+nIWHzjS7vFFU3m3FRZ70MvsBwPQILAOUAQlw\/QrZuBJ5vlWsOkyezIOr1ecPBqfds68zmullzdlDvp5Cy34SzmnspXL+vdvsKEWJBIdbDnlw8Y8SYiLsAuks2P9lU35U4a8z7lM3gH92ymlhBgPs0FuiIsJ2foe1Y9XTq+Ol1CfGgFIaWKxQN2sFWiNq8kdFYPp1FS0CSPijd1gj86SuirRpTIoMWOzYLWsy3f5Q+y+dtGSTflOkbJpnv2Rwki5aIHBVCJEoLUmrBjlDA7ZJ1s29pR8hLGa5ofGSJewwcsKiofPLMl564YcWzf8iB39zlwCCofSFA1HeTK5kfHSDfl9qJuwpf+irP8hrO4fypv0bjY7S3XwxYKpEKe7ZUPrsObDOXcAMrmUN767nD0HzJmf\/y7FRvSREk36BH\/ro\/kwqILSs6zNM\/ZCiKhhV4WbA0stJtQEKvFx1vbyHou4bO3D9nIigc2sttNDOEHjuxBa\/YPrYd9S1CNCAfqBIgtdPTQMkfIBTTxtYc2D5erOErm+4e1aTxsGP\/WMJ6udo+kz6gNV04svgfLBx\/BcmLxHUc2Bx3JbtbQLtaIYkpawx5+e2tQ05qVYsyStmQZtG1kESwBFNrYLZ8Cnzd5IW0jW7Y\/1KDmooXbUFk0o9M2iwLMJiABHOUTs2hX+x2xg5HcJdc9S5OmbeVYsZ+q6zHJl41faFIw5j6M0\/m6fLV4W9GRwZo5rSE2LOjIuLiZjOW\/LCFbRabTRci0BG80zPLyR2hkrGgWxaAWwJe+I+On8FuYzbQJvQ0FPwGp1SfATd25l\/CUvopyXc5Cdk3YQOI2Vteev7kz1BS1oOFMVxZyO8qCXqssiFqERFPmLsbwyYWIHShCFAFEAfZm\/HM58\/httTBAkYVJ\/pxmy5HBm3FY0D8S+AnawXcizjEgljO1TQiQCS0HEPZnTgBrAmg5L87UBtBErG3yf8lNYrI2+8Vv1xh47BcA\/wXxSs5+LgRtTzt5mOSC04BgbX512T4Xc6o0F9kbuzaQSTndCJNyptlPl5QuJIf2gQ5wJRdnD2f344dDnaJGp06gQc+cbuZXN8M8pNNHlaj7t+cEw4SJ5YkVE8RuTby56p4CdLrvy0G2tJaCSZMR9Tvuyt1MKgp086TAKmXjyE4JiEWoOCrAp5tsZGARjxsbEnvhlTTgtaIoJHgJrAPLdLaOm9jK4XwvslasAXhO02KVRUnRBFdAWcz3AteGfp8AV0oHWAXhNRy4asrTBlhVXi+QRbZTKzY80ArxDbCFMMamAxDAAJuuNjwiVCvaApGNwdbDEdX8viB5fXMxvtKDSLVbPZjckSzdLdRGS5F5GmiJgsDC5B3QEkPP8sgHoqVMqB1o2f6SYIN7QyxuQGzF75X14esqzddZ98r14fJnxjbMaTpNZ3SmYOttmhUgfQY0KbK3Jr6WvYEwmYFZuAzZSkK3kpXvHlPC8tXDQCXsPlldS1iMnP4lLG5A9GAlLIbe77KEZZshO0tYGR+dSjzs4I+vXbGrs2V9upFh3ezybZ0uGqnarXzFhLxP+Yod52PLV+zYx2Kr28DWat9AItbP6zihmeQjOpavAhzB8zrhx3hUYuDpTWg3HLQ2FO1Qx4ptJnmixhsAYfXk6aOsIg\/BHkiLPbsSBPHgaCvEf0IQ961jcVArishOnGwMth6oqOaT4MPqWLVbV+dMyelm1nQ3z3N1+ilTUAMyiYutAErf2QNCJmHbRFKwA98fMstkmpaWaNWw6kuOZuErNi\/LnlxNcG7d1BXgXG\/tSdD7IUrCZBqFsSa\/cPc4mVxOflQxms7W0yJ6iinfeIujZVSoQJ3RKMnXWZhMaROrq661EXpT2Qqks3WSRMl8P06Lrd7hcFpTnjZOq\/J64bSDq4N074HTQvwQOO04taItOL055HpYpnrgA6Fa7VYTqnclT3cb9fBayUQNyHbtwPKDLch2nMam\/hGQ7WLPcoIhIFuCURtqh9Mp4\/PXEhSn3CibGyVA1WOxkEnt9yCxTMwdSLzrJSD+Unvlxz66Qvnf13RdUQrNcxqSxYh37sPF7AhEET7FGnTGXx7Hj+MLBc2XNExYJObFDMzoy2GyuDuKb2qpUBnS5L0wLg7IDEcba8rThnFVXi\/a2A2CWrHhaWMh\/ve3R+fB2uy9+3R1BOkBpurPD92sUzvWY6I3srC7fcOz0J5vW667xUKToHEQ6IjpwLcdC7lDsNACzA7X8GWGduKhxUEa+RJ2j3iJ+McDf3WMSyD\/2ybyf6Fh1g\/482i5Zrgw2zqCwQ9eVfocDfsbKh6P+oPQ2JryjkH9fnS2ksXvQWlL1P\/d0doK6rdR2xsB1BkUP5blVjvWY7o3MrC7fXpst0znbpCv8tyDQn5PnvsYyN9guvXQW58e34XesFm3rzbK9lswXWcvB7mXLcy+Gd+yKuFsfNskxjNarLMEsCM56QZuX9Mii6a5frEudauoFh2wFidKh2NaNOVpg7UqrxfT4rvV537vwbQI8UMwLb5fK9rCtJQjrYc7quUfyLCo3eoxLHWOdDdMmwZvJJwGsgbEsRDe4lb6I2vgBpaPh+BWdJG1zKVOhHjjJV1CXB7+7kqjzMOi5eS5QOP6oLaEvNvrKxYxDz+Hdzxyzq4awNyQx3H5x7OHcYNCkRBbQbK4z2S6CJM5BQXNi36YvK3gJ8C0En8eOHQnDscPeOpOU6D+sTtVYC8CBdrIr1UbnkGR8hUKxQModoBj8v9M55s2PkOb1Kq2sB7bw66HaA039CU+WKzrQXWjW13agyfCEWbp8R1beahz2AMixFB5+LPKkNjWILyHOib74FPmVSfaA0K3+\/lmOo3yKE12fx4jUJd\/ENNA3SydZ+FyScHG65LE2JTJkffs8eGnm7vLhy8K\/N7RZ5plYQwWab6KijBu7EUWNGF74ls7lRKKv1+\/0UwfiLd0\/qS0wnWxSDN26EUbkQehNnQFHoXI\/cgNNWffg90oEbmmNxDwFtBmwPwC7aPguI2O2Bz8\/\/3j392Bqz8nMT6\/vL+8mXTHZE1WYiudjjBSj5jYkZw6+Gyzpf0WSeF6\/fEZOeWtXf1IigqlOoL0fs5hGSXRMlwZOy8KLMdAzC9+nWiOcksgIco1T00hy+WOI39cJHbKGzEcSympXPZQCRZyYRe57CjAe8it9MVkULkeLr9h5hvi\/eSKrWIRzWhAuSUXxcOZw9ZQHt4teQgfNyRju7yccABviJWbKL2C8nKBIaKtqk2aso\/1Brs6tYxeWXkqtmNfvV5LyEElPnS48pMgeecna5y0owgSA0BQjSKeetcoROq9qAfOGIrQJsTdmFC1Jz0iz32Ah8vr8dXlZMwmi5vJGNyNJ+Nf6iXRtiOEGyD74IU7gn\/5wvzguMIPDtGwAHoCTYhLjp24xWV3F2ERHlIW2y4Ty9SVze4KY3ER3xAq362TQxoTHAh9eaO7tkSWDP11pSyLDqnreLB0sGx2V9nxnKEcfM2+7C8OKe3ZMoR5o7vCnj1QCH8fJZrfY5R5v1EItvXY9iF2E6KKLJz+KpNSYpRHlBtK1PuQlfJvQ0hsPoXZ9vzuEXnviSrTcyxvt1A98wPbcjQdsPEZz0HLfU7qD217Q2pP652NqwRbbd\/5ielBBwRDmV\/f5qXK7Gu8KJF0zN\/5ZcIh8x3bGWz8sai1tqR2dwFsuAAhPQe0Hfk96APo9vNAVYWzO8Phlsy+IYCRdva3nG876AHk9o0CpfomnitvoGvK7esHokd5tJ33OOgE7L2HCxSp7Q5oEKJfm\/xneSH1q1kqWz5\/lYpKevSrKT9hwg7xPWx7vksQMTYLgoTfg8dzFpa\/L2tF2RalAWqUBk3DxQlp+fkPEi+xIkz9Qmf7mi+0b6zNNGmILXVhYgOrF48yufml\/XbresHiEXtrxUIadnRYsXgOGmTJcjm5vx2fP9zwrZv78dX4\/GF8ASY3Fy27KdvEJH\/KzSIqYtpUzd1QDe36cIL1Kz+T0avZKvH+0WUiH7WbXyb1HcC8Sx7nUZKPjPRrwuj2Sgdm4AvYse3v2Dt1GZazrCwOgl4WX05uHx\/uW0yOktWa7Tro2xxo7Z2xDYxkZq5XAjK3jvbqme7AnoP9+LDH9nRddDR+U5924\/Ocf\/ObUDFxdjQb9Rvx8c+XF+PJ+bjFbvoSzSj\/\/k3fcKQ16vzozNX4gmW32N1k\/3ccxqHzHbJZRzeQfqP\/OLm6Of9z2+ivkzid\/tpp9InW6LeUS\/8H\"><title id=\"nx-dag-g04-stem-probability-statistics-catmodel-t\">Probability and statistics in Health Nexus: catastrophe model pipeline<\/title><desc id=\"nx-dag-g04-stem-probability-statistics-catmodel-d\">A stochastic event catalogue drives the hazard module; exposure and vulnerability turn intensity into damage; the financial module applies policy terms; event and year loss tables give exceedance curves, PML and TVaR. Design of the process, not a record of a run.<\/desc><\/svg><\/div><\/div><p class=\"nx-caption\"><strong>Fig. 01<\/strong><span>Nodes carry their type, metrics and state; connectors show what feeds what; the gates are the validation steps; the timeline is one application. Design of the method's use, not a record of a run.<\/span><\/p><\/div><\/div><\/section><section id=\"nx-sec-data\" class=\"nx-section\" data-hue=\"health\" data-variant=\"split\" aria-labelledby=\"nx-s-8\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">07<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Data<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">What the method consumes.<\/h2><p class=\"nx-lede\">Datasets and records used by probability and statistics on this platform, with the role each plays and the label its values carry. Sources are listed in <a class=\"nx-link\" href=\"https:\/\/therisk.global\/health-nexus\/research\/sources\/\">the source register<\/a>.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"register\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Data<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Data\">DHS and MICS microdata<\/td><td data-label=\"Role\">Design-based and model-based estimation<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Vital registration series<\/td><td data-label=\"Role\">Baseline modelling<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OFFICIAL<\/span><\/td><\/tr><tr><td data-label=\"Data\">Serosurveys<\/td><td data-label=\"Role\">Calibration of ascertainment<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Routine reporting with completeness metadata<\/td><td data-label=\"Role\">Adjustment for missing reports<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"data\" data-nxf-kind=\"data\" data-nxf-resource=\"nexus:\/\/gcri\/G04\/stem\/probability-statistics\/#data\" data-nxf-title=\"Method record\" data-nxf-height=\"520\" hidden><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gcri\/G04\/stem\/probability-statistics\/#evidence\" data-nxf-title=\"Evidence\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-checks\" class=\"nx-section\" data-hue=\"health\" data-tone=\"alt\" aria-labelledby=\"nx-s-9\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Validation<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">How results are checked.<\/h2><p class=\"nx-lede\">Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Check<\/th><th scope=\"col\">How it is done<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Check\">Design-based variance<\/td><td data-label=\"How it is done\">Standard errors account for clustering, stratification and survey weights<\/td><\/tr><tr><td data-label=\"Check\">Posterior predictive checks<\/td><td data-label=\"How it is done\">Models must reproduce the observed data before they are used to estimate or predict<\/td><\/tr><tr><td data-label=\"Check\">Sensitivity to priors and baselines<\/td><td data-label=\"How it is done\">Results are recomputed under alternative assumptions and the spread is reported<\/td><\/tr><\/tbody><\/table><\/div><p class=\"nx-note nx-mt-3\">Inference depends on assumptions about independence, distribution and sampling that may not hold. Small or biased samples limit reliability.<\/p><\/div><\/div><\/section><section id=\"nx-sec-connections\" class=\"nx-section\" data-hue=\"health\" data-variant=\"stack\" aria-labelledby=\"nx-s-10\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">09<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connections<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">Topics that rely on this method.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\" data-nav=\"true\"><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/health-nexus\/topics\/health-system-continuity\/\"><span>Health system continuity<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/health-nexus\/topics\/one-health\/\"><span>One Health<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/health-nexus\/topics\/infectious-threats\/\"><span>Infectious threats<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/health-nexus\/topics\/health-equity\/\"><span>Health equity<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/health-nexus\/topics\/environmental-health\/\"><span>Environmental health<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/health-nexus\/topics\/health-supply-chains\/\"><span>Health supply chains<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/health-nexus\/topics\/digital-health-risk\/\"><span>Digital health risk<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/health-nexus\/topics\/healthcare-infrastructure\/\"><span>Healthcare infrastructure<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-stewardship\" class=\"nx-section\" data-hue=\"health\" data-tone=\"alt\" data-variant=\"stack\" aria-labelledby=\"nx-s-11\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">10<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Stewardship<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">Who maintains the method.<\/h2><p class=\"nx-lede\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/systems-modelling\/\">Systems Science and Modelling<\/a> stewards probability and statistics for the Consortium and reviews how it is applied across platforms.<\/p><\/div><div class=\"nx-section-body\"><p class=\"nx-p\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/probability-statistics\/\">Canonical page: Probability and statistics<\/a><\/p><\/div><\/div><\/section><section id=\"nx-s-12-sec\" class=\"nx-section\" data-hue=\"health\" aria-labelledby=\"nx-s-12\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">11<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connected<\/span><\/div><h2 id=\"nx-s-12\" class=\"nx-section-title\">Where this page leads across the Consortium.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-dest\"><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/stem\/mathematics\/probability-statistics\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Canonical method page<\/span><span class=\"nx-dest-host\">GCRI<\/span><\/span><span class=\"nx-dest-title\">Probability and statistics<\/span><span class=\"nx-dest-why\">Platform application links to the canonical method page.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/systems-modelling\/stem\/probability-statistics\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Steward platform<\/span><span class=\"nx-dest-host\">GCRI<\/span><\/span><span class=\"nx-dest-title\">Statistics for modelling<\/span><span class=\"nx-dest-why\">Systems 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