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GCRI · Labs

Statistics for research design

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.

پرسیارێک بنێرە Back to STEM foundation of Labs
01تۆمارکردن

Statistics for research design on the record.

Method recordSTEM method
  1. ڕێگائەگەر و ئامار
  3. Applied inLabs
  4. داتا4 inputs
  6. Canonical pageSTEM Commons
02Application

What probability and statistics does here.

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 سیستەمە. They make the evidence behind risk decisions explicit and reproducible. The canonical description is on STEM Commons.

Sample size and power planning

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.

Meta-analysis with bias assessment

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.

Multiverse and specification analysis

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.

03ڕێگا و بەڵگە

How statistics for research design is modelled, measured and tested.

مۆدێلەکە، گریمانەکانی، داتاکانی و نادڵنیاییەکەی، بەو شێوەیەی زانا یان ئەندازیارێک دەیپشکنێت. پێکهاتەکان ڕێگا بڵاوکراوەکان دەگرنەبەر. بەهاکانی کێشراو نموونەییە، نەک دۆزینەوە.

مۆدێل، هاوکێشەکان و گریمانەکان

The model structure used for statistics for research design, the assumptions it makes and how each is tested.

مەترسیی توخمی iRi = f(Hi, Ei, Vi, Ci)مەترسیدەر، ڕووبەڕووبوون، لاوازی و توانای مامەڵەکردن
زیانی ساڵانەی چاوەڕوانکراوEAL = ∑k pk · Lkبەسەر ڕووداوەکانی k بە ئەگەری ساڵانەی pk
سیستەمە هاوبەستەکانxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error
گریمانەبۆچی دروست دەکرێتچۆن تاقی دەکرێتەوە
مەترسیدەر و ڕووبەڕووبوون لە ناو پێشهاتێکدا سەربەخۆنئینتێگراڵی زیان بە ئاسانی دەهێڵێتەوەبەراورد بکە لەگەڵ گونجاندنی کۆپیولا لەسەر تۆمارە هاوبەشەکان
هاوبەستییەکان لە نزیک خاڵی کارکردن هێڵینڕێگە دەدات شۆکەکان بڵاو ببنەوە لە Aلە دەرەوەی مەودای هێڵیی شڵەژێنە و بەراورد بکە
چەمکەکانی لاوازی لە شوێنێکەوە بۆ شوێنێکی تر دەگوازرێنەوەچەمکی ناوخۆیی بە دەگمەن بەردەستەلەگەڵ زیانە چاودێریکراوەکان پشتڕاستی بکەرەوە کە هەن
تێکردنەکان
کۆمەڵە داتای وەشانکراو لەگەڵ ڕەسەنایەتی
دەرچووەکان
دابەشبوون، نەک یەک ژمارە
کۆد
بڵاوکراوەتەوە لەگەڵ کارتی مۆدێل و تاقیکردنەوەکان
بەرهەمهێنانەوە
هەمان داتا، کۆد، ژینگە و تۆو
فۆرمۆلەکردنی مەترسی و دۆخ-بۆشایی ستاندارد؛ ئەو گریمانانەی لیست کراون ئاسایین بۆ ڕێگاکە، بۆ پێداچوونەوە ڕاگەیەندراون، نەک ئەنجام.
پرەنسیپە ڕێنماییەکانی FAIR · GO FAIR و FORCE11W3C PROV · World Wide Web Consortiumڕێنمایی بۆ مامەڵەی یەکسان لەگەڵ نادڵنیاییەکان · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
04داتا

What the method consumes.

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 the source register.

داتاڕۆڵناونیشان
Registered analysis plansFixing hypotheses and tests in advanceDECLARED
Raw experiment and trial dataRe-analysis and diagnostic checksچاودێریکراو
Published effect sizesMeta-analytic poolingREPORTED
Simulation studiesPower and error-rate planningمۆدێلکراو
05پرۆسە

چۆن پرۆسەکە کار دەکات.

Process explorer · Process

Statistics for research design: Catastrophe model pipeline

10 steps5 قۆناغ

پرۆسە

Statistics for research design: Catastrophe model pipeline

Catastrophe models usually have hazard, vulnerability, exposure and financial modules, and produce loss tables, تێکڕای زیانی ساڵانە, exceedance curves, PML and TVaR.

  1. 01 Hazard

    1. 01Event catalogueDataset · Model developerThe hazard module holds a large catalogue of simulated events that represents a wide range of plausible scenarios.
    2. 03Hazard moduleModel · Model developerFor each event the hazard module gives the local hazard intensity that the vulnerability module reads.

  2. 02 Exposure and damage

    1. 02ڕووبەڕووبوونDataset · Ethics committeeThe exposure module houses the portfolio data, such as location-specific information.
    2. 04Vulnerability moduleModel · Model developerThe vulnerability module quantifies the expected damage to the exposures from an event.

  3. 03 Financial

    1. 05Financial moduleModel · Ethics committeeThe financial module calculates losses from all event scenarios. Ground-up, gross and net losses differ as shown below.
    2. 06Event loss tableDataset · Ethics committeeAn event loss table lists each event with its annual occurrence probability and its loss for the portfolio.
    3. 07Year loss tableDataset · Ethics committeeSome models also sample simulated years from event losses. This outline does not describe their format.

  4. 04 Metrics

    1. 08Exceedance probability curvesModel · Ethics committeeThe 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.
    2. 09AAL, PML and TVaRModel · Ethics committeeAverage annual loss sums event probability times loss. PML is one point on the occurrence curve.

  5. 05 Use of results

    1. 10Use of resultsDecision · Ethics committeeThe tail metrics are used when considering a portfolio’s exposure and the level of catastrophe reinsurance an insurer requires.

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.

06پشتڕاستکردنەوە

How results are checked.

Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.

بپشکنەHow it is done
Planned versus reported testsEvery reported test is matched to the plan; unplanned tests are labelled exploratory
Assumption diagnosticsResiduals, independence and distributional assumptions are checked and reported
Multiplicity controlCorrections or hierarchical testing are applied where many outcomes or subgroups are tested
Independent re-analysisA second analyst reproduces key estimates from the raw data

Inference depends on assumptions about independence, distribution and sampling that may not hold. Small or biased samples limit reliability.

07Connections

Topics that rely on this method.

08Stewardship

Who maintains the method.

زانستی سیستەمەکان و مۆدێلسازی stewards probability and statistics for the Consortium and reviews how it is applied across platforms.

Canonical page: Probability and statistics

09پەیوەست

ئەو شوێنانەی ئەم لاپەڕەیە لە ناو کۆنسۆرتیۆم بۆیان دەبات.

10پرسیارەکان

پرسیار و وەڵام.

What are the limits of probability and statistics in this field?
Inference depends on assumptions about independence, distribution and sampling that may not hold. Small or biased samples limit reliability.
How are results checked?
Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
ئەو canonical method page lists every platform that applies it.
Reportsبەشداربە

لەم لاپەڕەیەوە بەڵگە پێشکەش بکە.

Reports is the Consortium’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.

GCRILabsبەڵگەی Labsڕێگا و تەکنیکی

هەرگیز داتای کەسی، وردەکاریی هەستیاری ژێرخان یان هیچ شتێک مەنێرە کە مافی بڵاوکردنەوەت نییە. بەشدارییەک پێش بەکارهێنان پێداچوونەوەی بۆ دەکرێت؛ ناردنی ماف نابەخشێت بە نووسەرایەتی یان بەڕێوەبردنی ڕاگەیاندن، و پاڵپشتی دارایی هەرگیز دۆزینەوەیەک ناکڕێت.

Labs

ڕێگایەک، کۆمەڵە داتایەک یان پرسیارێک بهێنە.

زانیاری و پرسیار ئەمڕۆ کراوەن. پرسیارێک ژمارەی ئاماژەی پێدەدرێت و هەنگاوی دواتر دیاری دەکات؛ داواکاری، پارەدان، تەرخانکردنی کۆمپیوتەر و تۆمارکردن تەنیا کاتێک دەکرێنەوە کە تیم، مەرج و توانای پێداچوونەوەیان هەبێت.

ڕێگایەک پێشنیار بکە بینینی هەموو ڕێگاکانی پەیوەندی
یاسایی

نەخشە، داتا و وێنەکانی ئەم ماڵپەڕە بۆ ناوەڕۆک و زانیاریی گشتی پیشان دراون. ئامۆژگاری، پێشبینی یان ئاگادارکردنەوە نین؛ سنوورەکانی زانیارییەکە لە ڕەتکردنەوەی بەرپرسیارێتی و ئاگاداریی داتادا دەستنیشان کراون.