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F5\/s9XCNR1uN\/AxTKs3WvWkdlU7cCiV5Zv5L+e+Iz8kNRstXuIBMKEoNJuW9iBEaiBD7GUrJUA8K54DZlvngEyrwjEkdxY4yR02YTXK5RtRVWEND6RpPmTI+ZAi50O\/cgcSQq4Bw6BQKsFvYE+WbGhpDJOInEljC5g7DH0mjmkzwx5b0QnTmsAs0b9QQYMnwLRFrUoOPbPsfIPzwCkoA6uJ7T8lig+dB0LIiZjRQ3YWOEmJn8WbklDLKd42uTG6\/2PkqThEnuDVQjspPSXyIyCIZ6WHPcxpkjF0PaaaB2cpqGcvpG0b3Gv212tvY2leAhYhsyjMeAgniT1C0rUaJms1Klur4UvWaoRXY8GoRwJNRgXuxaxB4k23GhVQO\/2mo2Z64Omq550LDa\/ghTvUDo\/HqZqTm3mYEQyHMWUfuLY43brM4MYVY+L6QrFEkDAdzsg477r3V6bf\/otKLrzB+gkMQEXuPULGOfR2h9oYUHkXn8YrgJTrT7OdwqmE2hinE2MgElIEgK\/XmxOSKk1P9GSWYi44bewjaZof1cY+isY+po3Neh+Fg+k5FQ1GcBTWtG3V23r9W7OJfzZ+40t4ctwEXUYRimJMrq+d6OnwzmlxfLiOwLjM+72Vp\/tnz6CKsOsOtdvOXjfZ6F31YHuht9408zqVz7JKuvq3QkN5oXEL0bPG+qcXGOtnmWAJqHGyR8gsAfqJRIdZ72TbXP8EopEd+L5QOnzO9GVFA33rMLXQZz9BP\/eUmuEZTpV0clYR\/T6lMTvx3QmnKBeRNcmUgKVftc9S\/fAZdFOZjy0\/06ePyaEj2nE+Gnvq0cgiTdM6ihKoWACVqGf1K07+Yw+p3VMtwitfS4\/2BduSEAYjw6aex0VvU8iDzIiCeZyOYUGa5uIoTMawkCsT9hZ9XjXueX28yj2Wcdox6PY916d8iLmjnyUq5g6A8l1v\/GxjUWUBaAGVSzph4AJKBTMaKdtEIi+AqsCyoi2WxRyFUYGCSrOwEDO2e+Myuev\/f9CTnJdHLTim+EJZ\/FhpPXfcGcLmY4VujblhJhoVC3H7IFhrAg\/KQQFJHzmhmOYs348KaWF8UzzmD9WofBC2FTnQCY0mUp3lk14PibEJv169VFnGbIT7elqSQTGlQ+WTU9WCg3A\/4\/GUNE2aHbhp4mbi92i\/1+orXKmgdCt6P4aWDzZHxfGWN9HsmzlFG5ENm485QEZiRa3n2rdUx1450yGSW\/6Zunl9h1YOnUd51K1XfI+EEOVE1pChDqp9XZz+u8af9Jrr1+gipBGrgUstnaM2kqsbVo5tgLAZ5gZW3m8M3ipVX+qo9nV62Prt36iN3tPFNK51meU7VuTEyQfTysHbw4UXIS6siKVgvmDLgmhOK5XkgvCxnd+HXP4YssaC\/4GdP6w\/SpHgL65hXwhNe+NXvBqS5CFXpPGrNO6Npcq9sQANfP1XaC55l7YqBI16683xtiCDV9AIWAJ5C418ncavm0cp1sBgMynrV30o5BYz+c6CkIyEKJOxKW4t1YXrGFjQ\/84Y+mMFp+OEASCfw4iylYieWQmL+I2Ij7veeiU3CG1EHFCtN5zkFgAfZYDPKhcg33pVCRKrBCReASR\/a0AhFXqafbqiOR+UMlSXWirhOytgUL5LPTqjnnSyxUYcei7T0BolgNky0IlK3ONKrXLwNa4fZEIL2D69IyshlV5yGAqJxWUD0U1DOoL1mqA4lHanBXk\/nQxopIVAU9OdssunJDn4gzsJlb4kcgd1xip6GCxyJxpu0+RcO6yjdg3Vtai36K\/XiAOjhuoLnBx2kbIiNznzTGYOLeg\/mBv29VovBVS5SbTGXR5AX1t21OHsNI13o17b12Ea7dLV4IU30lvDr9L99pTlls42ihMSH81Wd1fC\/Ua0\/ZNUkFWLHLBL\/6kJGVTu0ISEoa1CTThw+HuE\/IRXPxHfZFTzE0vLRo5LTB\/WKHXdlVNUaAmlGuUIoUPfQAY+p7++YBFM8YxApRyoZM9\/a1U1tzWWwuWKpdq72LZC0OwpTVW0tS1A\/i\/hm0LVVLR09CUtZ0iP8u5tCtgTx7YqIsyXpQqzdUmaJc09ONQjybfwLlAFU4b0vxWh06e7bS0AIY+OqO\/UjmsgB8kd09drFZ9YQmhouQlp6FLPkVstERvX62zXSbjkvdgsLaGDLyPsY0IrT5KS\/OS1VEfjEh1V5isAhsCp5wG718bbiqqd9D\/VsC\/qqIHqEch\/De7uArosaKxEwHfcSeZnyU\/QvGQ504fCyuER9FfdTCTvpjawbqkGyQOiyj+WmKfCiMYxWKcSEHSkKwmvUAYFLt\/KQhHGqTVrkw1QMEI\/xwj97eOxPEkNpoxBEIrJi6TmsKZZUiU\/Zl6gjJEgvGVr42WfV94u8CrUFgVTyaay+jatW\/C1WGSWCkYjtF6nL2oIrxbbJoqSTlWTaZi2Wz5dgdGwK8q+0yiyXL8RDIdiX45xpbtIrmziWVIWD6CUy\/yFczvOz+04jqDDfuX4\/e2Nkl\/scXFJE2lFyvqLCmw1owxbCSGjDvdoleLWCIxD5ZqSxFJpiSLpUtzlKKoiZ5mJLMK9tLWD3len3\/vq6Eb\/54OC5JHwBjj9CpFTjifKIkZ0ES4iaqYOpRbOteERLUoESwPhXqsv6JOEvJA+o8wbREdgxpFW5gXxpU2mqVNRJhE9kw\/ydT3RxCUiZp56j7QLAZFbcmGw4MJdUH6CBDEXr27JbTarr6dah2hfndUvG1zXOntfHYxhonrmRHKbwboLDcMRkCT0eUgupCo4VR+3dO2iMcQHoHLmw\/tMWg3+NQzmWqtxq\/8COiP1wLSojzTr58+4PoYx3h61AKhl6s2u3AP56j6gXkVxHrxa7usm2FMYBTaNY9cfSR77GLqx+MUiy6biJ+ei4ueEssi1RWUbgnX76XvwFxJO\/Z0dmmnVwS2iL1BQs0xBXeaGx0+rPX9bP51TBQlHPSk5maVExM1iDWAWpcSLzxgIxB14U28iqoSsUe6OzMYRjceB57SNw9dKFNoSHN2W7MhNR7\/kHqERTXXgLust2VYw2glUPFErnojoKtY7Yf1nJqKooU+fkb3yxj9JPp7xdRHU4iX2Vu4Ss9vPFCeKVWFwW4GE04Z6ukSNtrWpcpUR1Cc9hITE7zX7UtH+PnCoalJ+EuOekSe1ADfoC9dioV+YyTMJfwJDvvh4ndMjNHX1ff2p0n2bKydmuz8fvlLXawwDdYfaEyEkdVXbeOSJb7EnvArVETVTdjcjTXOWGaxnqbF+Bshiy2CGJ7zibwDIZYxOcv9sR6j1q8K8i3UWWtpHwr3TPrcYfq7bQn\/VIfJJeTUfjXSw\/zOjG0+kaT5lMHlKYfKkwuRPvBprfypWLAHOdLEhRENv4vrhK\/1EJONJ3e8UJV09751\/TF4WMXC409tCmES5w0UCkx2BAtz16ojM8OokVyc1sV13Ph5tj\/xn401CnAv1NmPtRHq8y2OKwhQFCR7BaYXbHHZa6JSje9oUnN03mxET+767w2FXQAzp3WLYx\/NG2C5+UXjzRTVD6qrEvdfUW32FvN+SpnmbLdotLNpFStlp7zZbuJJTat7nfL1WywuSJwd+x2eTH\/JnPuT8F4Di54yxlPTJz69MoYB405VRDRYtFRiYNfBoqtCaWCxyF+rJ9hwY1Tk7slIonHNGdU81i\/bOgbPx8+IucaTEL9WDZj121E35n8ekb2qX9TwmvFPlTxGZ9pLxg2NanDC6CyGHQBQI30FX3VsppeNUJm01FYcoGNMDOwqyXcqSbXrGSEB7D6xfNoRICk5cc6POMoYWY3Z0ls4yTlt0KTljvZiVnoBFU0JkcWnHpUcXacCMrKUqsKXzIa0Mf9gRoZeM9RHG+siOvHT2j3ysp1TzaO8xWYYpI8gH845BJwPqgPfvhKKMctxmsRjlAczJmGhjHIDfs+6r8ogwBbKcKVCa9Xu0r2R8Wa\/hRWp9L90KQvTwpeQBvgvtSPcXlIsc2yHwMZelcpH4W8S95JTKAaCzxGHUB79oPhUwveofYEbw+OkHI3TtwAuiRmxD8+2aY0WPONEDcicfRkRAVAb6KR9\/4lgGigBeoPOp8ymJ6u0gaAi1kcedL9O\/SiDbd+45fQ0WHy6UMSxalBbiqYyurAKRWRVMIuHk48oSE2FyFnI6zx4ymDrOcoGSEVAl2EYIDxoP4eRz6oi5iFba\/J34jXP1LMfJVcrKtdUG5GGhZGsO5ECFY3\/pXJIiMFk15BtigYSWIh0Azn0sjFPOv8HHKOCHTc7tq2pJqJRVVF1n+BJzdJerCrt2pzkeLOawqXbuAGkP42Vht1UQI16+wYLRyKO1VDO3wy2He+XsVCxCgZK1EgsFrvlbih2O5vm0G8+5lQPVa8vJrhzKLNdri5RRbLNJHca9ZP\/\/IU4u0MUfRHGk9LosiKwRR64rRifaNKUdDK9ELRAO\/oAdIPZnh\/8WGMt\/ilBONY7yj+ycdFkepa2CXXLIq1zyp3v+qTsO5r7wVkx113tXDK+KruAyaFg4g++i9ZKSzV0gQZFeM2xv0Nh6KVFWvOErp9eM0JVIDM\/yG8I5S03i9AdOlVEAng9L8L2JQ88C52Ofp0lA7iQMImbxQ4VgZA6tzLDBV5qLbTRK2OU4TdWRQuWy46RM+TLnoHP5o2MyL9Nl37vs7F2xzp5D83GyPK1WRQD+mEZB7ZhrbKTjMWhtIF1CmVhqU5\/u\/tpwwV+2skxkTXiXoJOoKEOLg+HRywyvblhZriY1LZO+f+S4sxonasK\/RkXcGrdPo1EUTENU42TAswaQnuQkDKkVWb5Na8FQBA2F1oii4yNBY2qAtSQxBdRSApFssppKU4gkFMfX8O3o64Go9nxbfJOWN\/XWih5\/oCW+x8tb6vL0NkpbXw8cd3a8D3h0w57f\/Pzwcdm5YhXomy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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-n1aioyjk.nxe :where(*,*::before,*::after){box-sizing:inherit}#nxe-n1aioyjk.nxe :where(section,header,footer,nav,aside){display:block;margin:0 0 32px}#nxe-n1aioyjk.nxe :where(h1,h2,h3,h4){line-height:1.2;margin:24px 0 12px;font-weight:600}#nxe-n1aioyjk.nxe :where(p,ul,ol,dl,table,figure,details){margin:0 0 16px}#nxe-n1aioyjk.nxe :where(ul,ol){padding-left:20px}#nxe-n1aioyjk.nxe a{color:inherit;text-decoration:underline;text-underline-offset:2px}#nxe-n1aioyjk.nxe table{border-collapse:collapse;width:100%;display:block;overflow-x:auto}#nxe-n1aioyjk.nxe :where(th,td){padding:6px 8px;text-align:left;vertical-align:top;border-bottom:1px solid;border-color:inherit}#nxe-n1aioyjk.nxe svg{display:none}#nxe-n1aioyjk.nxe svg.nx-i{display:inline-block;width:18px;height:18px;vertical-align:-3px}#nxe-n1aioyjk.nxe :where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n1aioyjk.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-scene nx-es-scene\" data-scene=\"text\" data-ground=\"none\" data-es-org=\"gcri\" aria-labelledby=\"nx-scene-title\"><div class=\"nx-scene-intro nx-es-intro\"><p class=\"nx-eyebrow\"><abbr title=\"The Global Centre for Risk and Innovation\">GCRI<\/abbr> \u00b7 Labs<\/p><span class=\"nx-scene-rule\" aria-hidden=\"true\"><\/span><h1 id=\"nx-scene-title\" class=\"nx-scene-title\">Statistics for research design<\/h1><p class=\"nx-scene-lede\">Probability and statistics decide what a study can conclude: how large a sample must be, where signal ends and noise begins, how big and how precise an effect is. They are also a common source of failure, through underpowered designs, misread significance tests and analytic flexibility, which is why this lab treats statistical planning as part of the protocol.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/therisk.global\/nexus-labs\/write-to-us\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/therisk.global\/nexus-labs\/stem\/\">Back to STEM foundation of Labs<\/a><\/div><\/div><\/section><section id=\"nx-s-1-sec\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-1\"><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\">Register<\/span><\/div><h2 id=\"nx-s-1\" class=\"nx-section-title\">Statistics for research design on the record.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-es-fig\" data-es-layout=\"console\"><div class=\"nx-hero-panel\"><div class=\"nx-register\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Method record<\/strong><\/span><span class=\"nx-eyebrow\">STEM method<\/span><\/div><ol class=\"nx-register-rows\"><li><div><span class=\"nx-register-k\">Method<\/span><span class=\"nx-register-v\">Probability and statistics<\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Steward<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/systems-modelling\/\">Systems Science and Modelling<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Applied in<\/span><span class=\"nx-register-v\"><a class=\"nx-link nx-term\" href=\"https:\/\/docs.therisk.global\/organization\/programme\/labs\" data-tc=\"Labs\" data-tk=\"Nexus Standards\" data-td=\"The research pillar of the programme, one of six (OR-11, OR-12)\">Labs<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Data<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"#nx-sec-data\">4 inputs<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Checks<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"#nx-sec-checks\">4 validation steps<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Canonical page<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/probability-statistics\/\">STEM Commons<\/a><\/span><\/div><\/li><\/ol><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-application\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" 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\">02<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Application<\/span><\/div><h2 id=\"nx-s-2\" 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 research and evidence, methods and modelling and innovation, statistical methods quantify the likelihood and uncertainty of losses across interconnected <a class=\"nx-link nx-term\" href=\"https:\/\/docs.therisk.global\/organization\/systems\" data-tc=\"Systems\" data-tk=\"Nexus Standards\" data-td=\"The twenty Nexus systems, NXS-CMD to NXS-LAB\">systems<\/a>. 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\"><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\">Sample size and power planning<\/h3><p class=\"nx-p nx-mt-1\">The smallest effect worth detecting is set from prior evidence or practical relevance, and the sample is sized to detect it with stated power, or to estimate it with stated precision, before recruitment begins.<\/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=\"m12.83 2.18a2 2 0 0 0-1.66 0L2.6 6.08a1 1 0 0 0 0 1.83l8.58 3.91a2 2 0 0 0 1.66 0l8.58-3.9a1 1 0 0 0 0-1.83Z\"\/><path d=\"m22 17.65-9.17 4.16a2 2 0 0 1-1.66 0L2 17.65\"\/><path d=\"m22 12.65-9.17 4.16a2 2 0 0 1-1.66 0L2 12.65\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Meta-analysis with bias assessment<\/h3><p class=\"nx-p nx-mt-1\">Effects from many studies are pooled with random-effects models, heterogeneity is examined, and funnel plots and selection models test whether small-study effects or publication bias distort the pooled estimate.<\/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=\"M14 2v4a2 2 0 0 0 2 2h4\"\/><path d=\"M4.268 21a2 2 0 0 0 1.727 1H18a2 2 0 0 0 2-2V7l-5-5H6a2 2 0 0 0-2 2v3\"\/><path d=\"m9 18-1.5-1.5\"\/><circle cx=\"5\" cy=\"14\" r=\"3\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Multiverse and specification analysis<\/h3><p class=\"nx-p nx-mt-1\">Every defensible combination of exclusions, covariates and outcome definitions is run and reported together, so readers see how far a conclusion depends on analytic choices.<\/p><\/article><\/div><\/div><\/div><\/section><section id=\"nx-sec-audience\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-3\"><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\">Methods and evidence<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">How statistics for research design is modelled, measured and tested.<\/h2><p class=\"nx-lede\">The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-au\" data-au-voice=\"science\" data-au-n=\"1\"><figure class=\"nx-au-c\" data-au-fig=\"method-panel\"><h3 class=\"nx-h4\">Model, equations and assumptions<\/h3><p class=\"nx-p nx-au-what\">The model structure used for statistics for research design, the assumptions it makes and how each is tested.<\/p><div class=\"nx-au-eqs\"><div class=\"nx-au-eq\"><span class=\"nx-au-k\">Risk of element i<\/span><code>R<sub>i<\/sub> = f(H<sub>i<\/sub>, E<sub>i<\/sub>, V<sub>i<\/sub>, C<sub>i<\/sub>)<\/code><span class=\"nx-au-n\">hazard, exposure, vulnerability and coping capacity<\/span><\/div><div class=\"nx-au-eq\"><span class=\"nx-au-k\">Expected annual loss<\/span><code>EAL = &#8721;<sub>k<\/sub> p<sub>k<\/sub> &#183; L<sub>k<\/sub><\/code><span class=\"nx-au-n\">over events k with annual probability p<sub>k<\/sub><\/span><\/div><div class=\"nx-au-eq\"><span class=\"nx-au-k\">Coupled systems<\/span><code>x<sub>t+1<\/sub> = A x<sub>t<\/sub> + B u<sub>t<\/sub> + &#949;<sub>t<\/sub><\/code><span class=\"nx-au-n\">A carries the couplings, u the shock, &#949; the error<\/span><\/div><\/div><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Assumption<\/th><th scope=\"col\">Why it is made<\/th><th scope=\"col\">How it is tested<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Assumption\">Hazard and exposure are independent within an event<\/td><td data-label=\"Why it is made\">Keeps the loss integral tractable<\/td><td data-label=\"How it is tested\">Compare with a copula fit on joint records<\/td><\/tr><tr><td data-label=\"Assumption\">Couplings are linear near the operating point<\/td><td data-label=\"Why it is made\">Lets shocks propagate through A<\/td><td data-label=\"How it is tested\">Perturb beyond the linear range and compare<\/td><\/tr><tr><td data-label=\"Assumption\">Vulnerability curves transfer between places<\/td><td data-label=\"Why it is made\">Local curves are rarely available<\/td><td data-label=\"How it is tested\">Validate against observed losses where they exist<\/td><\/tr><\/tbody><\/table><\/div><dl class=\"nx-au-kv\"><div><dt>Inputs<\/dt><dd>versioned datasets with provenance<\/dd><\/div><div><dt>Outputs<\/dt><dd>distributions, never single numbers<\/dd><\/div><div><dt>Code<\/dt><dd>released with the model card and tests<\/dd><\/div><div><dt>Reproduction<\/dt><dd>same data, code, environment and seed<\/dd><\/div><\/dl><figcaption class=\"nx-caption\"><span>Standard risk and state-space formulations; the assumptions listed are typical of the method, stated for review, not results.<\/span><\/figcaption><\/figure><\/div><div class=\"nx-chips nx-au-fw\" aria-label=\"Frameworks cited\"><span class=\"nx-chip\">FAIR guiding principles \u00b7 GO FAIR and FORCE11<\/span><span class=\"nx-chip\">W3C PROV \u00b7 World Wide Web Consortium<\/span><span class=\"nx-chip\">Guidance on consistent treatment of uncertainties \u00b7 IPCC<\/span><span class=\"nx-chip\">CEOS Analysis Ready Data \u00b7 Committee on Earth Observation Satellites<\/span><\/div><\/div><\/div><\/section><section id=\"nx-sec-data\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-4\"><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\">Data<\/span><\/div><h2 id=\"nx-s-4\" 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\/nexus-labs\/research\/\">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\">Registered analysis plans<\/td><td data-label=\"Role\">Fixing hypotheses and tests in advance<\/td><td data-label=\"Label\"><span class=\"nx-chip\">DECLARED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Raw experiment and trial data<\/td><td data-label=\"Role\">Re-analysis and diagnostic checks<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Published effect sizes<\/td><td data-label=\"Role\">Meta-analytic pooling<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Simulation studies<\/td><td data-label=\"Role\">Power and error-rate planning<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"data\" data-nxf-kind=\"data\" data-nxf-resource=\"nexus:\/\/gcri\/G08\/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\/G08\/stem\/probability-statistics\/#evidence\" data-nxf-title=\"Evidence\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-s-5-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-5\"><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\">Process<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">How the process runs.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><div class=\"nx-pe\" id=\"nx-pe-catmodel\" data-nx-pe=\"nx-pe-catmodel\" data-active=\"catmodel\"><div class=\"nx-pe-shell\"><p class=\"nx-pe-eb\">Process<\/p><h2 class=\"nx-pe-title\">Statistics for research design: Catastrophe model pipeline<\/h2><p class=\"nx-pe-cap\">Catastrophe models usually have hazard, vulnerability, exposure and financial modules, and produce loss tables, <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 average-annual-loss\" data-tk=\"Nexus risk vocabulary\" data-td=\"The long-run expected loss per year, averaged over all possible events; it equals the area under the loss exceedance curve.\" data-ts=\"NRS-C1\" data-tr=\"draft\">average annual loss<\/a>, exceedance curves, PML and TVaR.<\/p><ol class=\"nx-pe-steps\"><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">01 Hazard<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">01<\/span><span class=\"nx-pe-st\">Event catalogue<\/span><span class=\"nx-pe-k\">Dataset \u00b7 Model developer<\/span><span class=\"nx-pe-sm\">The hazard module holds a large catalogue of simulated events that represents a wide range of plausible scenarios.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">03<\/span><span class=\"nx-pe-st\">Hazard module<\/span><span class=\"nx-pe-k\">Model \u00b7 Model developer<\/span><span class=\"nx-pe-sm\">For each event the hazard module gives the local <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 hazard-intensity\" data-tk=\"Nexus risk vocabulary\" data-td=\"The physical or functional strength of a hazard at a location, expressed through a measure appropriate to that hazard, such as depth, speed, shaking or concentration.\" data-ts=\"NRS-C1\" data-tr=\"draft\">hazard intensity<\/a> that the vulnerability module reads.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">02 Exposure and damage<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">02<\/span><span class=\"nx-pe-st\">Exposure<\/span><span class=\"nx-pe-k\">Dataset \u00b7 Ethics committee<\/span><span class=\"nx-pe-sm\">The exposure module houses the portfolio data, such as location-specific information.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">04<\/span><span class=\"nx-pe-st\">Vulnerability module<\/span><span class=\"nx-pe-k\">Model \u00b7 Model developer<\/span><span class=\"nx-pe-sm\">The vulnerability module quantifies the expected damage to the exposures from an event.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">03 Financial<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">05<\/span><span class=\"nx-pe-st\">Financial module<\/span><span class=\"nx-pe-k\">Model \u00b7 Ethics committee<\/span><span class=\"nx-pe-sm\">The financial module calculates losses from all event scenarios. Ground-up, gross and net losses differ as shown below.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">06<\/span><span class=\"nx-pe-st\">Event loss table<\/span><span class=\"nx-pe-k\">Dataset \u00b7 Ethics committee<\/span><span class=\"nx-pe-sm\">An event loss table lists each event with its annual occurrence probability and its loss for the portfolio.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">07<\/span><span class=\"nx-pe-st\">Year loss table<\/span><span class=\"nx-pe-k\">Dataset \u00b7 Ethics committee<\/span><span class=\"nx-pe-sm\">Some models also sample simulated years from event losses. This outline does not describe their format.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">04 Metrics<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">08<\/span><span class=\"nx-pe-st\"><a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 exceedance-probability\" data-tk=\"Nexus risk vocabulary\" data-td=\"The probability that a quantity will reach or exceed a given level over a given period.\" data-ts=\"NRS-C1\" data-tr=\"draft\">Exceedance probability<\/a> curves<\/span><span class=\"nx-pe-k\">Model \u00b7 Ethics committee<\/span><span class=\"nx-pe-sm\">The occurrence curve gives the chance that the largest loss in a year exceeds an amount. The aggregate curve does so for the sum of losses.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">09<\/span><span class=\"nx-pe-st\">AAL, PML and TVaR<\/span><span class=\"nx-pe-k\">Model \u00b7 Ethics committee<\/span><span class=\"nx-pe-sm\">Average annual loss sums event probability times loss. PML is one point on the occurrence curve.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">05 Use of results<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">10<\/span><span class=\"nx-pe-st\">Use of results<\/span><span class=\"nx-pe-k\">Decision \u00b7 Ethics committee<\/span><span class=\"nx-pe-sm\">The tail metrics are used when considering a portfolio&#8217;s exposure and the level of catastrophe reinsurance an insurer requires.<\/span><\/li><\/ol><\/li><\/ol><p class=\"nx-pe-foot\"><span>Outline of an external standard or method, from its public sources. It describes what the framework says, not a record of any run. It does not show that Nexus or any partner performs, certifies or approves a step. A gate is a step of the method, never a Nexus approval.<\/span><\/p><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-checks\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"inset\" aria-labelledby=\"nx-s-6\"><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\">Validation<\/span><\/div><h2 id=\"nx-s-6\" 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\" data-lv=\"ledger\"><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\">Planned versus reported tests<\/td><td data-label=\"How it is done\">Every reported test is matched to the plan; unplanned tests are labelled exploratory<\/td><\/tr><tr><td data-label=\"Check\">Assumption diagnostics<\/td><td data-label=\"How it is done\">Residuals, independence and distributional assumptions are checked and reported<\/td><\/tr><tr><td data-label=\"Check\">Multiplicity control<\/td><td data-label=\"How it is done\">Corrections or hierarchical testing are applied where many outcomes or subgroups are tested<\/td><\/tr><tr><td data-label=\"Check\">Independent re-analysis<\/td><td data-label=\"How it is done\">A second analyst reproduces key estimates from the raw data<\/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=\"project\" data-variant=\"split\" aria-labelledby=\"nx-s-7\"><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\">Connections<\/span><\/div><h2 id=\"nx-s-7\" 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\/nexus-labs\/topics\/research-protocols\/\"><span>Research protocols<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/field-and-lab-testbeds\/\"><span>Field and lab testbeds<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/ai-and-scientific-workflows\/\"><span>AI in scientific workflows<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-work-lab\/\"><span>Future of work lab<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-media-lab\/\"><span>Future of media lab<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-energy-and-water-labs\/\"><span>Future of energy and water labs<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-health-and-bio-labs\/\"><span>Future of health and biotechnology labs<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/reproducibility-and-negative-results\/\"><span>Reproducibility and negative results<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><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=\"project\" data-tone=\"alt\" 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\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Stewardship<\/span><\/div><h2 id=\"nx-s-8\" 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-9-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-9\"><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\">Connected<\/span><\/div><h2 id=\"nx-s-9\" 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\/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 Science and Modelling stewards this method.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><\/ul><\/div><\/div><\/section><section id=\"nx-s-10-sec\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-10\"><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\">Questions<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>What are the limits of probability and statistics in this field?<\/summary><div class=\"nx-answer\">Inference depends on assumptions about independence, distribution and sampling that may not hold. Small or biased samples limit reliability.<\/div><\/details><details><summary>How are results checked?<\/summary><div class=\"nx-answer\">Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.<\/div><\/details><details><summary>Are the values on this page real?<\/summary><div class=\"nx-answer\">The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method&#8217;s use, not a run; no result is reported here.<\/div><\/details><details><summary>Where else is this method applied?<\/summary><div class=\"nx-answer\">The <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/probability-statistics\/\">canonical method page<\/a> lists every platform that applies it.<\/div><\/details><\/div><\/div><\/div><\/section><section id=\"nx-sec-nr-gateway\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-nr-gateway-h\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">Reports<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Contribute<\/span><\/div><h2 id=\"nx-nr-gateway-h\" class=\"nx-section-title\">Contribute evidence from this page.<\/h2><p class=\"nx-lede\"><a class=\"nx-link\" href=\"https:\/\/docs.therisk.global\/organization\/programme\/reports\">Reports<\/a> is the Consortium&#8217;s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.<\/p><div class=\"nx-chips\"><span class=\"nx-chip\">GCRI<\/span><span class=\"nx-chip\">Labs<\/span><span class=\"nx-chip\">Labs Evidence<\/span><span class=\"nx-chip\">Methods and Technical<\/span><\/div><\/div><div class=\"nx-section-body\"><ul class=\"nx-dest\"><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/nexus-labs\/write-to-us\/contact\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Email<\/span><span class=\"nx-dest-host\">Gateway GW-GCRI<\/span><\/span><span class=\"nx-dest-title\">Send it from this page<\/span><span class=\"nx-dest-why\">Evidence, a correction, a case, a method, a review, a translation or profile input. 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