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آمار برای طراحی پژوهش

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.

01ثبت‌خانه

Statistics for research design on the record.

Method recordSTEM method
  1. روشاحتمال و آمار
  2. داده4 inputs
  3. 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 سامانه‌ها. 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.

03روش‌ها و شواهد

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

مدل، مفروضات، داده‌ها و عدم قطعیت آن، به شکلی که دانشمند یا مهندس بررسی می‌کند. ساختارها از روش‌های منتشرشده پیروی می‌کنند. مقادیر ترسیم‌شده تبیینی‌اند، نه یافته.

مدل، معادلات و مفروضات

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

ریسک عنصر iRi = f(Hi, Ei, Vi, Ci)مخاطره، مواجهه، آسیب‌پذیری و ظرفیت مقابله
زیان سالانهٔ مورد انتظارEAL = ∑k pk · Lkبر رویدادهای k با احتمال سالانهٔ pk
سامانه‌های جفت‌شدهxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error
فرضچرا ساخته می‌شودچگونه آزموده می‌شود
مخاطره و مواجهه در یک رویداد مستقل‌اندانتگرال زیان را تحت‌کنترل نگه می‌داردمقایسه با برازش مفصل بر سوابق مشترک
جفت‌شدگی‌ها نزدیک نقطهٔ کار خطی‌انداجازه می‌دهد شوک‌ها از طریق A منتشر شوندفراتر از دامنهٔ خطی آشفته کنید و مقایسه کنید
منحنی‌های آسیب‌پذیری میان مکان‌ها منتقل می‌شوندمنحنی‌های محلی به‌ندرت در دسترس‌انداعتبارسنجی در برابر زیان‌های مشاهده‌شده در جاهایی که وجود دارند
ورودی‌ها
مجموعه‌داده‌های نسخه‌دار با خاستگاه
خروجی‌ها
توزیع‌ها، هرگز تک‌رقم‌ها
کد
منتشرشده با کارت مدل و آزمون‌ها
بازتولید
همان داده، کد، محیط و بذر
فرمول‌بندی‌های استاندارد ریسک و فضای حالت؛ مفروضات فهرست‌شده نمونهٔ روش‌اند، برای داوری بیان شده‌اند و نتیجه نیستند.
اصول راهنمای FAIR · GO FAIR و FORCE11W3C PROV · کنسرسیوم وب جهانیGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
04داده

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.

دادهنقشبرچسب
Registered analysis plansFixing hypotheses and tests in advanceDECLARED
Raw experiment and trial dataRe-analysis and diagnostic checksمشاهده‌شده
Published effect sizesMeta-analytic poolingگزارش‌شده
Simulation studiesPower and error-rate planningمدل‌سازی‌شده
05فرایند

نحوهٔ اجرای فرایند.

Process explorer · Process

Statistics for research design: Catastrophe model pipeline

10 steps5 مرحله

فرایند

Statistics for research design: Catastrophe model pipeline

Catastrophe models usually have hazard, vulnerability, exposure and financial modules, and produce loss tables, میانگین زیان سالانه, 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. 02مواجههDataset · 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. 08احتمال فراگذشت 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.

06اعتبارسنجی

How results are checked.

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

بررسیHow 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.

08تولیت

Who maintains the method.

علوم سامانه‌ها و مدل‌سازی stewards probability and statistics for the Consortium and reviews how it is applied across platforms.

09متصل

جایی که این صفحه در سراسر کنسرسیوم به آن می‌رسد.

10پرسش‌ها

پرسش و پاسخ.

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?
رصدخانهٔ canonical method page lists every platform that applies it.
گزارش‌هامشارکت

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گزارش‌ها 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.

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