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Search, Evaluate, and Read Evidence","\u002Fen\u002Facademic-writing\u002F02-reading-and-literature-matrix","en\u002Facademic-writing\u002F02-reading-and-literature-matrix",{"title":27,"path":28,"stem":29},"3. From Sources to Synthesis and Argument","\u002Fen\u002Facademic-writing\u002F03-arguments-outlines-and-paragraphs","en\u002Facademic-writing\u002F03-arguments-outlines-and-paragraphs",{"title":31,"path":32,"stem":33},"4. Literature Review and a Defensible Gap","\u002Fen\u002Facademic-writing\u002F04-writing-the-literature-review","en\u002Facademic-writing\u002F04-writing-the-literature-review",{"title":35,"path":36,"stem":37},"5. Research Design, Evidence, and Core Sections","\u002Fen\u002Facademic-writing\u002F05-core-sections-and-evidence","en\u002Facademic-writing\u002F05-core-sections-and-evidence",{"title":39,"path":40,"stem":41},"6. Citation, Paraphrasing, Integrity, and AI","\u002Fen\u002Facademic-writing\u002F06-citation-paraphrasing-and-integrity","en\u002Facademic-writing\u002F06-citation-paraphrasing-and-integrity",{"title":43,"path":44,"stem":45},"7. Revision, Review, and Submission","\u002Fen\u002Facademic-writing\u002F07-revision-style-and-submission","en\u002Facademic-writing\u002F07-revision-style-and-submission",{"title":47,"path":48,"stem":49},"8. Research Workbook","\u002Fen\u002Facademic-writing\u002F08-literature-review-checklist-and-template","en\u002Facademic-writing\u002F08-literature-review-checklist-and-template",{"title":51,"path":52,"stem":53},"9. Research Workflow and Tools","\u002Fen\u002Facademic-writing\u002F09-tools-for-academic-writing-and-literature-management","en\u002Facademic-writing\u002F09-tools-for-academic-writing-and-literature-management",{"title":55,"path":56,"stem":57},"10. Worked Project — From Question to Defensible Conclusion","\u002Fen\u002Facademic-writing\u002F10-worked-example-from-block-structure-to-question-chain","en\u002Facademic-writing\u002F10-worked-example-from-block-structure-to-question-chain",{"title":59,"path":60,"stem":61,"children":62,"page":249},"Accounting","\u002Fen\u002Faccounting","en\u002Faccounting",[63,69,87,93,193],{"title":64,"path":65,"stem":66,"children":67},"Accounting — From Evidence to Decisions","\u002Fen\u002Faccounting\u002F00-index","en\u002Faccounting\u002F00-index",[68],{"title":64,"path":65,"stem":66},{"title":70,"path":71,"stem":72,"children":73},"Accounting Appendix","\u002Fen\u002Faccounting\u002Fappendix","en\u002Faccounting\u002Fappendix\u002Findex",[74,75,79,83],{"title":70,"path":71,"stem":72},{"title":76,"path":77,"stem":78},"Worked Examples and Error Diagnosis","\u002Fen\u002Faccounting\u002Fappendix\u002F24-examples-pitfalls","en\u002Faccounting\u002Fappendix\u002F24-examples-pitfalls",{"title":80,"path":81,"stem":82},"Accounting Glossary","\u002Fen\u002Faccounting\u002Fappendix\u002F26-glossary","en\u002Faccounting\u002Fappendix\u002F26-glossary",{"title":84,"path":85,"stem":86},"Reading and Evidence Map","\u002Fen\u002Faccounting\u002Fappendix\u002F27-reading-map","en\u002Faccounting\u002Fappendix\u002F27-reading-map",{"title":88,"path":89,"stem":90,"children":91},"Northstar Record-to-Decision Capstone","\u002Fen\u002Faccounting\u002Fcapstone","en\u002Faccounting\u002Fcapstone\u002Findex",[92],{"title":88,"path":89,"stem":90},{"title":94,"path":95,"stem":96,"children":97},"Financial Accounting","\u002Fen\u002Faccounting\u002Ffinancial-accounting","en\u002Faccounting\u002Ffinancial-accounting\u002Findex",[98,99,117,131,165,179],{"title":94,"path":95,"stem":96},{"title":100,"path":101,"stem":102,"children":103},"1. Foundations","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002Findex",[104,105,109,113],{"title":100,"path":101,"stem":102},{"title":106,"path":107,"stem":108},"Objectives and Qualitative Characteristics","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F01-objectives-qualitative-characteristics","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F01-objectives-qualitative-characteristics",{"title":110,"path":111,"stem":112},"Equation and Elements","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F02-equation-elements","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F02-equation-elements",{"title":114,"path":115,"stem":116},"Accrual Basis, Estimates and Periods","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F03-accounting-bases-principles","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F03-accounting-bases-principles",{"title":118,"path":119,"stem":120,"children":121},"2. Recording Transactions","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions","en\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002Findex",[122,123,127],{"title":118,"path":119,"stem":120},{"title":124,"path":125,"stem":126},"Double-Entry and Debit\u002FCredit","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F04-double-entry-dr-cr","en\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F04-double-entry-dr-cr",{"title":128,"path":129,"stem":130},"Accounting Cycle","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F05-accounting-cycle","en\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F05-accounting-cycle",{"title":132,"path":133,"stem":134,"children":135},"3. Measurement and Adjustments","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002Findex",[136,137,141,145,149,153,157,161],{"title":132,"path":133,"stem":134},{"title":138,"path":139,"stem":140},"Revenue Recognition","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F07-revenue-recognition","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F07-revenue-recognition",{"title":142,"path":143,"stem":144},"Inventory","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F08-inventory","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F08-inventory",{"title":146,"path":147,"stem":148},"Receivables and Expected Credit Losses","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F09-receivables","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F09-receivables",{"title":150,"path":151,"stem":152},"Property, Plant and Equipment","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F10-ppe","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F10-ppe",{"title":154,"path":155,"stem":156},"Intangible Assets","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F11-intangibles","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F11-intangibles",{"title":158,"path":159,"stem":160},"Leases","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F12-leases","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F12-leases",{"title":162,"path":163,"stem":164},"Current and Deferred Income Tax","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F13-income-tax","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F13-income-tax",{"title":166,"path":167,"stem":168,"children":169},"4. Reporting and Cash","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash","en\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002Findex",[170,171,175],{"title":166,"path":167,"stem":168},{"title":172,"path":173,"stem":174},"Linked Financial Statements","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F06-financial-statements","en\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F06-financial-statements",{"title":176,"path":177,"stem":178},"Cash, Reconciliation and Internal Control","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F14-cash-control","en\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F14-cash-control",{"title":180,"path":181,"stem":182,"children":183},"5. Analysis and Comparison","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison","en\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002Findex",[184,185,189],{"title":180,"path":181,"stem":182},{"title":186,"path":187,"stem":188},"Ratio Analysis","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F15-ratio-analysis","en\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F15-ratio-analysis",{"title":190,"path":191,"stem":192},"IFRS versus US GAAP","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F25-ifrs-gaap","en\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F25-ifrs-gaap",{"title":194,"path":195,"stem":196,"children":197},"Management Accounting","\u002Fen\u002Faccounting\u002Fmanagement-accounting","en\u002Faccounting\u002Fmanagement-accounting\u002Findex",[198,199,217,235],{"title":194,"path":195,"stem":196},{"title":200,"path":201,"stem":202,"children":203},"1. Cost Foundations","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002Findex",[204,205,209,213],{"title":200,"path":201,"stem":202},{"title":206,"path":207,"stem":208},"Cost Concepts and Behaviour","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F16-cost-concepts","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F16-cost-concepts",{"title":210,"path":211,"stem":212},"Costing Systems","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F17-costing-systems","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F17-costing-systems",{"title":214,"path":215,"stem":216},"Variable and Absorption Costing","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F19-variable-vs-absorption","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F19-variable-vs-absorption",{"title":218,"path":219,"stem":220,"children":221},"2. Planning and Control","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002Findex",[222,223,227,231],{"title":218,"path":219,"stem":220},{"title":224,"path":225,"stem":226},"Cost–Volume–Profit Analysis","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F18-cvp-analysis","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F18-cvp-analysis",{"title":228,"path":229,"stem":230},"Budgeting and Variance Analysis","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F20-budgeting-variances","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F20-budgeting-variances",{"title":232,"path":233,"stem":234},"Performance Measurement","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F21-performance-measurement","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F21-performance-measurement",{"title":236,"path":237,"stem":238,"children":239},"3. Decisions and Investment","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment","en\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002Findex",[240,241,245],{"title":236,"path":237,"stem":238},{"title":242,"path":243,"stem":244},"Short-Term Decisions","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F22-short-term-decisions","en\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F22-short-term-decisions",{"title":246,"path":247,"stem":248},"Capital Budgeting","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F23-capital-budgeting","en\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F23-capital-budgeting",false,{"title":251,"path":252,"stem":253,"children":254,"page":249},"Business Analytics","\u002Fen\u002Fbusiness-analytics","en\u002Fbusiness-analytics",[255,261,279,305,335,365,383,400],{"title":256,"path":257,"stem":258,"children":259},"Business Analytics — From Data to Defensible Action","\u002Fen\u002Fbusiness-analytics\u002F00-index","en\u002Fbusiness-analytics\u002F00-index",[260],{"title":256,"path":257,"stem":258},{"title":262,"path":263,"stem":264,"children":265},"0. Decision and Data Foundations","\u002Fen\u002Fbusiness-analytics\u002F00-intro","en\u002Fbusiness-analytics\u002F00-intro\u002Findex",[266,267,271,275],{"title":262,"path":263,"stem":264},{"title":268,"path":269,"stem":270},"Decision Framing","\u002Fen\u002Fbusiness-analytics\u002F00-intro\u002F01-decision-framing","en\u002Fbusiness-analytics\u002F00-intro\u002F01-decision-framing",{"title":272,"path":273,"stem":274},"Data Contracts","\u002Fen\u002Fbusiness-analytics\u002F00-intro\u002F02-data-contracts","en\u002Fbusiness-analytics\u002F00-intro\u002F02-data-contracts",{"title":276,"path":277,"stem":278},"Reproducible Analytics Workflow","\u002Fen\u002Fbusiness-analytics\u002F00-intro\u002F03-reproducible-workflow","en\u002Fbusiness-analytics\u002F00-intro\u002F03-reproducible-workflow",{"title":280,"path":281,"stem":282,"children":283},"1. Descriptive Analytics","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive","en\u002Fbusiness-analytics\u002F01-descriptive\u002Findex",[284,285,289,293,297,301],{"title":280,"path":281,"stem":282},{"title":286,"path":287,"stem":288},"Distributions and Exploratory Data Analysis","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F04-distributions-eda","en\u002Fbusiness-analytics\u002F01-descriptive\u002F04-distributions-eda",{"title":290,"path":291,"stem":292},"KPIs and Denominators","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F05-kpis-denominators","en\u002Fbusiness-analytics\u002F01-descriptive\u002F05-kpis-denominators",{"title":294,"path":295,"stem":296},"Segments, Cohorts and Funnels","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F06-segmentation-cohorts-funnels","en\u002Fbusiness-analytics\u002F01-descriptive\u002F06-segmentation-cohorts-funnels",{"title":298,"path":299,"stem":300},"Visual Evidence and Data Stories","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F07-visualisation-story","en\u002Fbusiness-analytics\u002F01-descriptive\u002F07-visualisation-story",{"title":302,"path":303,"stem":304},"Experiments and Causal Boundaries","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F08-experiments-causal-boundary","en\u002Fbusiness-analytics\u002F01-descriptive\u002F08-experiments-causal-boundary",{"title":306,"path":307,"stem":308,"children":309},"2. Predictive Analytics","\u002Fen\u002Fbusiness-analytics\u002F02-predictive","en\u002Fbusiness-analytics\u002F02-predictive\u002Findex",[310,311,315,319,323,327,331],{"title":306,"path":307,"stem":308},{"title":312,"path":313,"stem":314},"Validation, Baselines and Leakage","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F09-validation-leakage","en\u002Fbusiness-analytics\u002F02-predictive\u002F09-validation-leakage",{"title":316,"path":317,"stem":318},"Regression and Forecasting","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F10-regression-forecasting","en\u002Fbusiness-analytics\u002F02-predictive\u002F10-regression-forecasting",{"title":320,"path":321,"stem":322},"Classification and Calibration","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F11-classification-calibration","en\u002Fbusiness-analytics\u002F02-predictive\u002F11-classification-calibration",{"title":324,"path":325,"stem":326},"Trees and Ensembles","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F12-trees-ensembles","en\u002Fbusiness-analytics\u002F02-predictive\u002F12-trees-ensembles",{"title":328,"path":329,"stem":330},"Decision Metrics and Thresholds","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F13-decision-metrics-thresholds","en\u002Fbusiness-analytics\u002F02-predictive\u002F13-decision-metrics-thresholds",{"title":332,"path":333,"stem":334},"Explainability, Monitoring and Drift","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F14-explainability-drift","en\u002Fbusiness-analytics\u002F02-predictive\u002F14-explainability-drift",{"title":336,"path":337,"stem":338,"children":339},"3. Prescriptive Analytics","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive","en\u002Fbusiness-analytics\u002F03-prescriptive\u002Findex",[340,341,345,349,353,357,361],{"title":336,"path":337,"stem":338},{"title":342,"path":343,"stem":344},"Decision-Making under Uncertainty","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F15-decision-uncertainty","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F15-decision-uncertainty",{"title":346,"path":347,"stem":348},"Linear Optimisation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F16-linear-optimization","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F16-linear-optimization",{"title":350,"path":351,"stem":352},"Inventory and Allocation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F17-inventory-allocation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F17-inventory-allocation",{"title":354,"path":355,"stem":356},"Queueing and Simulation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F18-queueing-simulation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F18-queueing-simulation",{"title":358,"path":359,"stem":360},"Pricing and Revenue Management","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F19-pricing-experimentation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F19-pricing-experimentation",{"title":362,"path":363,"stem":364},"Causal Targeting and Policy Learning","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F20-causal-targeting","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F20-causal-targeting",{"title":366,"path":367,"stem":368,"children":369},"4. Deployment and Governance","\u002Fen\u002Fbusiness-analytics\u002F04-deployment","en\u002Fbusiness-analytics\u002F04-deployment\u002Findex",[370,371,375,379],{"title":366,"path":367,"stem":368},{"title":372,"path":373,"stem":374},"Data Products and Monitoring","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F21-data-products-monitoring","en\u002Fbusiness-analytics\u002F04-deployment\u002F21-data-products-monitoring",{"title":376,"path":377,"stem":378},"Governance, Fairness and Privacy","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F22-governance-fairness-privacy","en\u002Fbusiness-analytics\u002F04-deployment\u002F22-governance-fairness-privacy",{"title":380,"path":381,"stem":382},"Adoption and Business Value","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F23-adoption-value","en\u002Fbusiness-analytics\u002F04-deployment\u002F23-adoption-value",{"title":384,"path":385,"stem":386,"children":387},"Appendix and Revision Tools","\u002Fen\u002Fbusiness-analytics\u002Fappendix","en\u002Fbusiness-analytics\u002Fappendix\u002Findex",[388,389,393,397],{"title":384,"path":385,"stem":386},{"title":390,"path":391,"stem":392},"Formula and Decision Map","\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F24-formula-map","en\u002Fbusiness-analytics\u002Fappendix\u002F24-formula-map",{"title":394,"path":395,"stem":396},"Worked Examples and Pitfalls","\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F25-worked-examples-pitfalls","en\u002Fbusiness-analytics\u002Fappendix\u002F25-worked-examples-pitfalls",{"title":84,"path":398,"stem":399},"\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F26-reading-map","en\u002Fbusiness-analytics\u002Fappendix\u002F26-reading-map",{"title":401,"path":402,"stem":403,"children":404},"Capstone — The Evening Delivery Promise","\u002Fen\u002Fbusiness-analytics\u002Fcapstone","en\u002Fbusiness-analytics\u002Fcapstone\u002Findex",[405],{"title":401,"path":402,"stem":403},{"title":407,"path":408,"stem":409,"children":410,"page":249},"Financial Economic Time Series","\u002Fen\u002Ffinancial-economic-time-series","en\u002Ffinancial-economic-time-series",[411,417,423,429,435,441,447,453,459,465,471],{"title":412,"path":413,"stem":414,"children":415},"Financial and Economic Time Series — Course Guide","\u002Fen\u002Ffinancial-economic-time-series\u002F00-intro","en\u002Ffinancial-economic-time-series\u002F00-intro\u002Findex",[416],{"title":412,"path":413,"stem":414},{"title":418,"path":419,"stem":420,"children":421},"Three Versions of Time Series","\u002Fen\u002Ffinancial-economic-time-series\u002F01-bridge","en\u002Ffinancial-economic-time-series\u002F01-bridge\u002Findex",[422],{"title":418,"path":419,"stem":420},{"title":424,"path":425,"stem":426,"children":427},"Data, Clocks, and Transformations","\u002Fen\u002Ffinancial-economic-time-series\u002F02-data-transformations","en\u002Ffinancial-economic-time-series\u002F02-data-transformations\u002Findex",[428],{"title":424,"path":425,"stem":426},{"title":430,"path":431,"stem":432,"children":433},"Predictive Regressions and Persistent Predictors","\u002Fen\u002Ffinancial-economic-time-series\u002F03-predictive-regressions","en\u002Ffinancial-economic-time-series\u002F03-predictive-regressions\u002Findex",[434],{"title":430,"path":431,"stem":432},{"title":436,"path":437,"stem":438,"children":439},"Volatility, Tails, and Financial Risk","\u002Fen\u002Ffinancial-economic-time-series\u002F04-volatility-risk","en\u002Ffinancial-economic-time-series\u002F04-volatility-risk\u002Findex",[440],{"title":436,"path":437,"stem":438},{"title":442,"path":443,"stem":444,"children":445},"Unit Roots, Cointegration, and Error Correction","\u002Fen\u002Ffinancial-economic-time-series\u002F05-unit-roots-cointegration","en\u002Ffinancial-economic-time-series\u002F05-unit-roots-cointegration\u002Findex",[446],{"title":442,"path":443,"stem":444},{"title":448,"path":449,"stem":450,"children":451},"VAR, Structural Identification, and Local Projections","\u002Fen\u002Ffinancial-economic-time-series\u002F06-var-identification","en\u002Ffinancial-economic-time-series\u002F06-var-identification\u002Findex",[452],{"title":448,"path":449,"stem":450},{"title":454,"path":455,"stem":456,"children":457},"State Space, Mixed Frequency, and Nowcasting","\u002Fen\u002Ffinancial-economic-time-series\u002F07-state-space-nowcasting","en\u002Ffinancial-economic-time-series\u002F07-state-space-nowcasting\u002Findex",[458],{"title":454,"path":455,"stem":456},{"title":460,"path":461,"stem":462,"children":463},"Forecast Evaluation for Decisions","\u002Fen\u002Ffinancial-economic-time-series\u002F08-forecast-evaluation","en\u002Ffinancial-economic-time-series\u002F08-forecast-evaluation\u002Findex",[464],{"title":460,"path":461,"stem":462},{"title":466,"path":467,"stem":468,"children":469},"Integrated R Laboratory","\u002Fen\u002Ffinancial-economic-time-series\u002F09-r-laboratory","en\u002Ffinancial-economic-time-series\u002F09-r-laboratory\u002Findex",[470],{"title":466,"path":467,"stem":468},{"title":472,"path":473,"stem":474,"children":475},"Capstones, Data, and Reading Ladder","\u002Fen\u002Ffinancial-economic-time-series\u002F10-capstone-readings","en\u002Ffinancial-economic-time-series\u002F10-capstone-readings\u002Findex",[476],{"title":472,"path":473,"stem":474},{"title":478,"path":479,"stem":480,"children":481,"page":249},"Intro To Economics","\u002Fen\u002Fintro-to-economics","en\u002Fintro-to-economics",[482,486,490,494,498,502,506,510,514,518,522,526,530],{"title":483,"path":484,"stem":485},"Introduction to Economics — Decisions, Markets, and the Macroeconomy","\u002Fen\u002Fintro-to-economics\u002F00-intro","en\u002Fintro-to-economics\u002F00-intro",{"title":487,"path":488,"stem":489},"Chapter 1 — Choice, Opportunity Cost, and Trade","\u002Fen\u002Fintro-to-economics\u002F01-foundations","en\u002Fintro-to-economics\u002F01-foundations",{"title":491,"path":492,"stem":493},"Chapter 2 — Demand, Supply, Equilibrium, and Welfare","\u002Fen\u002Fintro-to-economics\u002F02-demand-and-supply","en\u002Fintro-to-economics\u002F02-demand-and-supply",{"title":495,"path":496,"stem":497},"Chapter 3 — Elasticity, Revenue, and Tax Incidence","\u002Fen\u002Fintro-to-economics\u002F03-elasticity","en\u002Fintro-to-economics\u002F03-elasticity",{"title":499,"path":500,"stem":501},"Chapter 4 — Firms, Market Power, and Market Failure","\u002Fen\u002Fintro-to-economics\u002F04-market-structures","en\u002Fintro-to-economics\u002F04-market-structures",{"title":503,"path":504,"stem":505},"Chapter 5 — GDP, Income, Wealth, and Welfare","\u002Fen\u002Fintro-to-economics\u002F05-gdp-and-wealth","en\u002Fintro-to-economics\u002F05-gdp-and-wealth",{"title":507,"path":508,"stem":509},"Chapter 6 — Inflation, Purchasing Power, and Labour Markets","\u002Fen\u002Fintro-to-economics\u002F06-inflation-and-unemployment","en\u002Fintro-to-economics\u002F06-inflation-and-unemployment",{"title":511,"path":512,"stem":513},"Chapter 7 — Productivity, Technology, and Economic Growth","\u002Fen\u002Fintro-to-economics\u002F07-economic-growth","en\u002Fintro-to-economics\u002F07-economic-growth",{"title":515,"path":516,"stem":517},"Chapter 8 — Money, Credit, and Banking","\u002Fen\u002Fintro-to-economics\u002F08-money-and-banking","en\u002Fintro-to-economics\u002F08-money-and-banking",{"title":519,"path":520,"stem":521},"Chapter 9 — Business Cycles, AD–AS, and Monetary Policy","\u002Fen\u002Fintro-to-economics\u002F09-monetary-policy-and-ad-as","en\u002Fintro-to-economics\u002F09-monetary-policy-and-ad-as",{"title":523,"path":524,"stem":525},"Chapter 10 — Fiscal Policy, Distribution, and Public Debt","\u002Fen\u002Fintro-to-economics\u002F10-fiscal-policy","en\u002Fintro-to-economics\u002F10-fiscal-policy",{"title":527,"path":528,"stem":529},"Chapter 11 — Trade, Capital Flows, and Exchange Rates","\u002Fen\u002Fintro-to-economics\u002F11-open-economy-and-exchange-rates","en\u002Fintro-to-economics\u002F11-open-economy-and-exchange-rates",{"title":531,"path":532,"stem":533},"Chapter 12 — Integrated Economic Analysis Studio","\u002Fen\u002Fintro-to-economics\u002F12-review-and-case-studies","en\u002Fintro-to-economics\u002F12-review-and-case-studies",{"title":535,"path":536,"stem":537,"children":538,"page":249},"Microeconometrics","\u002Fen\u002Fmicroeconometrics","en\u002Fmicroeconometrics",[539,557,563,581,603,629,651,673,691,709],{"title":540,"path":541,"stem":542,"children":543},"0. Causal Questions and Designs","\u002Fen\u002Fmicroeconometrics\u002F00-foundations","en\u002Fmicroeconometrics\u002F00-foundations\u002Findex",[544,545,549,553],{"title":540,"path":541,"stem":542},{"title":546,"path":547,"stem":548},"Causal Questions and Estimands","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F01-causal-question-estimands","en\u002Fmicroeconometrics\u002F00-foundations\u002F01-causal-question-estimands",{"title":550,"path":551,"stem":552},"Potential Outcomes and Experiments","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F02-potential-outcomes-experiments","en\u002Fmicroeconometrics\u002F00-foundations\u002F02-potential-outcomes-experiments",{"title":554,"path":555,"stem":556},"Causal Diagrams and Controls","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F03-dags-controls","en\u002Fmicroeconometrics\u002F00-foundations\u002F03-dags-controls",{"title":558,"path":559,"stem":560,"children":561},"Microeconometrics — Designing Credible Counterfactuals","\u002Fen\u002Fmicroeconometrics\u002F00-index","en\u002Fmicroeconometrics\u002F00-index",[562],{"title":558,"path":559,"stem":560},{"title":564,"path":565,"stem":566,"children":567},"1. Regression and Inference","\u002Fen\u002Fmicroeconometrics\u002F01-regression","en\u002Fmicroeconometrics\u002F01-regression\u002Findex",[568,569,573,577],{"title":564,"path":565,"stem":566},{"title":570,"path":571,"stem":572},"OLS as a Projection","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F04-ols-projection","en\u002Fmicroeconometrics\u002F01-regression\u002F04-ols-projection",{"title":574,"path":575,"stem":576},"FWL, Selection and Controls","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F05-fwl-selection-controls","en\u002Fmicroeconometrics\u002F01-regression\u002F05-fwl-selection-controls",{"title":578,"path":579,"stem":580},"Inference and Clustering","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F06-inference-clustering","en\u002Fmicroeconometrics\u002F01-regression\u002F06-inference-clustering",{"title":582,"path":583,"stem":584,"children":585},"2. Instruments and Panel Data","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel","en\u002Fmicroeconometrics\u002F02-iv-panel\u002Findex",[586,587,591,595,599],{"title":582,"path":583,"stem":584},{"title":588,"path":589,"stem":590},"Instrumental Variables","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F07-iv-identification","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F07-iv-identification",{"title":592,"path":593,"stem":594},"Weak Instruments and LATE","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F08-weak-iv-late","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F08-weak-iv-late",{"title":596,"path":597,"stem":598},"Panel Fixed Effects","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F09-panel-fixed-effects","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F09-panel-fixed-effects",{"title":600,"path":601,"stem":602},"Dynamic Panels and Limits","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F10-dynamic-panel","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F10-dynamic-panel",{"title":604,"path":605,"stem":606,"children":607},"3. Policy Evaluation Designs","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs","en\u002Fmicroeconometrics\u002F03-policy-designs\u002Findex",[608,609,613,617,621,625],{"title":604,"path":605,"stem":606},{"title":610,"path":611,"stem":612},"Difference-in-Differences","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F11-did-core","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F11-did-core",{"title":614,"path":615,"stem":616},"Staggered DID and Event Studies","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F12-staggered-event-studies","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F12-staggered-event-studies",{"title":618,"path":619,"stem":620},"Regression Discontinuity","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F13-rdd","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F13-rdd",{"title":622,"path":623,"stem":624},"Matching, Weighting and Overlap","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F14-matching-weighting","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F14-matching-weighting",{"title":626,"path":627,"stem":628},"Synthetic Control","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F15-synthetic-control","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F15-synthetic-control",{"title":630,"path":631,"stem":632,"children":633},"Module 4 — Choice and Limited Outcomes","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002Findex",[634,635,639,643,647],{"title":630,"path":631,"stem":632},{"title":636,"path":637,"stem":638},"Binary Choice","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F16-binary-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F16-binary-choice",{"title":640,"path":641,"stem":642},"Multinomial and Ordered Choice","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F17-multinomial-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F17-multinomial-choice",{"title":644,"path":645,"stem":646},"Count Outcomes","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F18-count-outcomes","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F18-count-outcomes",{"title":648,"path":649,"stem":650},"Censoring, Truncation and Selection","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F19-censoring-selection","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F19-censoring-selection",{"title":652,"path":653,"stem":654,"children":655},"Module 5 — Modern Causal Analysis","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal","en\u002Fmicroeconometrics\u002F05-modern-causal\u002Findex",[656,657,661,665,669],{"title":652,"path":653,"stem":654},{"title":658,"path":659,"stem":660},"Double Machine Learning","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F20-dml","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F20-dml",{"title":662,"path":663,"stem":664},"Heterogeneity and Policy Learning","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F21-heterogeneity-policy","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F21-heterogeneity-policy",{"title":666,"path":667,"stem":668},"Sensitivity and Partial Identification","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F22-sensitivity-partial-id","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F22-sensitivity-partial-id",{"title":670,"path":671,"stem":672},"External Validity and Transport","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F23-external-validity","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F23-external-validity",{"title":674,"path":675,"stem":676,"children":677},"Module 6 — From Data to Auditable Evidence","\u002Fen\u002Fmicroeconometrics\u002F06-workflow","en\u002Fmicroeconometrics\u002F06-workflow\u002Findex",[678,679,683,687],{"title":674,"path":675,"stem":676},{"title":680,"path":681,"stem":682},"Data Provenance","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F24-data-provenance","en\u002Fmicroeconometrics\u002F06-workflow\u002F24-data-provenance",{"title":684,"path":685,"stem":686},"Reproducibility and Reporting","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F25-reproducibility-reporting","en\u002Fmicroeconometrics\u002F06-workflow\u002F25-reproducibility-reporting",{"title":688,"path":689,"stem":690},"Paper and Evidence Audit","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F26-paper-audit","en\u002Fmicroeconometrics\u002F06-workflow\u002F26-paper-audit",{"title":692,"path":693,"stem":694,"children":695},"Appendix — Revision and Evidence Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix","en\u002Fmicroeconometrics\u002Fappendix\u002Findex",[696,697,701,705],{"title":692,"path":693,"stem":694},{"title":698,"path":699,"stem":700},"Formula and Design Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F27-formula-design-map","en\u002Fmicroeconometrics\u002Fappendix\u002F27-formula-design-map",{"title":702,"path":703,"stem":704},"Ten Worked Microeconometric Pitfalls","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F28-worked-pitfalls","en\u002Fmicroeconometrics\u002Fappendix\u002F28-worked-pitfalls",{"title":706,"path":707,"stem":708},"Reading and Software Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F29-reading-software-map","en\u002Fmicroeconometrics\u002Fappendix\u002F29-reading-software-map",{"title":710,"path":711,"stem":712,"children":713},"Capstone — Should Northbridge Expand Pathways?","\u002Fen\u002Fmicroeconometrics\u002Fcapstone","en\u002Fmicroeconometrics\u002Fcapstone\u002Findex",[714],{"title":710,"path":711,"stem":712},{"title":716,"path":717,"stem":718,"children":719,"page":249},"Microeconomics","\u002Fen\u002Fmicroeconomics","en\u002Fmicroeconomics",[720,726,732,738,744,750,756,762,768,774,780,786,792],{"title":721,"path":722,"stem":723,"children":724},"Advanced Microeconomics — Course Guide","\u002Fen\u002Fmicroeconomics\u002F00-intro","en\u002Fmicroeconomics\u002F00-intro\u002Findex",[725],{"title":721,"path":722,"stem":723},{"title":727,"path":728,"stem":729,"children":730},"Module 1 — Choice, Duality, and Revealed Preference","\u002Fen\u002Fmicroeconomics\u002F01-fundations","en\u002Fmicroeconomics\u002F01-fundations\u002Findex",[731],{"title":727,"path":728,"stem":729},{"title":733,"path":734,"stem":735,"children":736},"Module 2 — Comparative Statics and Welfare Measurement","\u002Fen\u002Fmicroeconomics\u002F02-comparative-statics","en\u002Fmicroeconomics\u002F02-comparative-statics\u002Findex",[737],{"title":733,"path":734,"stem":735},{"title":739,"path":740,"stem":741,"children":742},"Module 3 — Choice under Risk and Insurance","\u002Fen\u002Fmicroeconomics\u002F03-uncertainty","en\u002Fmicroeconomics\u002F03-uncertainty\u002Findex",[743],{"title":739,"path":740,"stem":741},{"title":745,"path":746,"stem":747,"children":748},"Module 4 — General Equilibrium and Welfare","\u002Fen\u002Fmicroeconomics\u002F04-general-equilibrium","en\u002Fmicroeconomics\u002F04-general-equilibrium\u002Findex",[749],{"title":745,"path":746,"stem":747},{"title":751,"path":752,"stem":753,"children":754},"Module 5 — Static, Dynamic, and Repeated Games","\u002Fen\u002Fmicroeconomics\u002F05-game-theory","en\u002Fmicroeconomics\u002F05-game-theory\u002Findex",[755],{"title":751,"path":752,"stem":753},{"title":757,"path":758,"stem":759,"children":760},"Module 6 — Oligopoly, Entry, and Algorithmic Pricing","\u002Fen\u002Fmicroeconomics\u002F06-oligopoly","en\u002Fmicroeconomics\u002F06-oligopoly\u002Findex",[761],{"title":757,"path":758,"stem":759},{"title":763,"path":764,"stem":765,"children":766},"Module 7 — Information Economics and Contracts","\u002Fen\u002Fmicroeconomics\u002F07-information-economics","en\u002Fmicroeconomics\u002F07-information-economics\u002Findex",[767],{"title":763,"path":764,"stem":765},{"title":769,"path":770,"stem":771,"children":772},"Module 8 — Mechanism Design and Auctions","\u002Fen\u002Fmicroeconomics\u002F08-mechanism-design","en\u002Fmicroeconomics\u002F08-mechanism-design\u002Findex",[773],{"title":769,"path":770,"stem":771},{"title":775,"path":776,"stem":777,"children":778},"Module 9 — Behavioural and Experimental Microeconomics","\u002Fen\u002Fmicroeconomics\u002F09-behavioural-economics","en\u002Fmicroeconomics\u002F09-behavioural-economics\u002Findex",[779],{"title":775,"path":776,"stem":777},{"title":781,"path":782,"stem":783,"children":784},"Module 10 — Externalities, Public Goods, and Collective Action","\u002Fen\u002Fmicroeconomics\u002F10-externalities-public-goods","en\u002Fmicroeconomics\u002F10-externalities-public-goods\u002Findex",[785],{"title":781,"path":782,"stem":783},{"title":787,"path":788,"stem":789,"children":790},"Module 11 — Matching and Market Design","\u002Fen\u002Fmicroeconomics\u002F11-market-design","en\u002Fmicroeconomics\u002F11-market-design\u002Findex",[791],{"title":787,"path":788,"stem":789},{"title":793,"path":794,"stem":795,"children":796},"Module 12 — Integrated Microeconomic Design Studio","\u002Fen\u002Fmicroeconomics\u002F12-review","en\u002Fmicroeconomics\u002F12-review\u002Findex",[797],{"title":793,"path":794,"stem":795},{"title":799,"path":800,"stem":801,"children":802},"Playground","\u002Fen\u002Fplayground","en\u002Fplayground\u002Findex",[803,804,808,812,816,820],{"title":799,"path":800,"stem":801},{"title":805,"path":806,"stem":807},"Typst Plugin Playground","\u002Fen\u002Fplayground\u002F01-typstex","en\u002Fplayground\u002F01-typstex",{"title":809,"path":810,"stem":811},"Citation Plugin Test","\u002Fen\u002Fplayground\u002F02-citation","en\u002Fplayground\u002F02-citation",{"title":813,"path":814,"stem":815},"Pyodide Playground","\u002Fen\u002Fplayground\u002F03-pyodide","en\u002Fplayground\u002F03-pyodide",{"title":817,"path":818,"stem":819},"WebR Playground","\u002Fen\u002Fplayground\u002F04-webr","en\u002Fplayground\u002F04-webr",{"title":821,"path":822,"stem":823},"Chart.js 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文献综述的构建与写作","\u002Fzh\u002Facademic-writing\u002F04-writing-the-literature-review","zh\u002Facademic-writing\u002F04-writing-the-literature-review",{"title":1056,"path":1057,"stem":1058},"8. 文献综述清单与模板","\u002Fzh\u002Facademic-writing\u002F08-literature-review-checklist-and-template","zh\u002Facademic-writing\u002F08-literature-review-checklist-and-template",{"title":1060,"path":1061,"stem":1062},"10. 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策略全流程","\u002Fzh\u002Fquant\u002F09-case-study","zh\u002Fquant\u002F09-case-study",{"title":1779,"path":1780,"stem":1781},"量化投资文献与软件图谱","\u002Fzh\u002Fquant\u002F10-reading-software-map","zh\u002Fquant\u002F10-reading-software-map",null,{"id":1784,"title":1710,"body":1785,"description":5414,"extension":5415,"features":1782,"hero":1782,"layout":1782,"locale":1782,"meta":5416,"navigation":1782,"path":1711,"published":5419,"seo":5420,"stem":1712,"__hash__":5421},"docs\u002Fzh\u002Fprob-and-stats\u002F02-statistics\u002F05-hypothesis\u002Findex.md",{"type":1786,"value":1787,"toc":5398},"minimark",[1788,1792,1803,1806,1810,1813,1832,1836,1839,2246,2249,2348,2351,2355,2878,2881,3137,3140,3144,3147,3392,3395,3538,3611,3615,3647,3898,3901,3904,4287,4350,4354,4424,4431,4434,4438,4750,4753,4757,4815,4818,4832,4835,4839,4872,4993,4996,5007,5010,5014,5107,5110,5179,5182,5185,5356,5359,5386],[1789,1790,1710],"h1",{"id":1791},"第十章假设检验原理",[1793,1794,1795],"blockquote",{},[1796,1797,1798,1802],"p",{},[1799,1800,1801],"strong",{},"案例："," 新疗法的平均改善为 0.2 个标准差。研究“不显著”可能表示无效，也可能只是样本太小。",[1796,1804,1805],{},"假设检验是在预先声明的错误控制规则下，用数据评价原假设。它不是自动判断“理论真假”的机器。",[1807,1808,1809],"h2",{"id":1809},"学习目标",[1796,1811,1812],{},"你应能：",[1814,1815,1816,1820,1823,1826,1829],"ol",{},[1817,1818,1819],"li",{},"写出原假设、备择假设与检验统计量；",[1817,1821,1822],{},"区分一类错误、二类错误、显著性水平与功效；",[1817,1824,1825],{},"准确解释 p 值；",[1817,1827,1828],{},"使用 Neyman–Pearson 与似然比思想；",[1817,1830,1831],{},"识别可选停止、多重检验与选择性报告。",[1807,1833,1835],{"id":1834},"_1-检验的四个组成","1. 检验的四个组成",[1796,1837,1838],{},"以均值为例：",[1840,1841,1844],"span",{"className":1842},[1843],"katex-display",[1840,1845,1848,1927],{"className":1846},[1847],"katex",[1840,1849,1852],{"className":1850},[1851],"katex-mathml",[1853,1854,1857],"math",{"xmlns":1855,"display":1856},"http:\u002F\u002Fwww.w3.org\u002F1998\u002FMath\u002FMathML","block",[1858,1859,1860,1922],"semantics",{},[1861,1862,1863,1874,1878,1881,1884,1890,1894,1898,1905,1907,1909,1913,1919],"mrow",{},[1864,1865,1866,1870],"msub",{},[1867,1868,1869],"mi",{},"H",[1871,1872,1873],"mn",{},"0",[1875,1876,1877],"mo",{},":",[1867,1879,1880],{},"μ",[1875,1882,1883],{},"=",[1864,1885,1886,1888],{},[1867,1887,1880],{},[1871,1889,1873],{},[1875,1891,1893],{"separator":1892},"true",",",[1895,1896],"mspace",{"width":1897},"2em",[1864,1899,1900,1902],{},[1867,1901,1869],{},[1871,1903,1904],{},"1",[1875,1906,1877],{},[1867,1908,1880],{},[1875,1910,1912],{"mathvariant":1911},"normal","≠",[1864,1914,1915,1917],{},[1867,1916,1880],{},[1871,1918,1873],{},[1867,1920,1921],{"mathvariant":1911},".",[1923,1924,1926],"annotation",{"encoding":1925},"application\u002Fx-tex","H_0:\\mu=\\mu_0,\\qquad\nH_1:\\mu\\ne\\mu_0.",[1840,1928,1931,2010,2029,2138,2197],{"className":1929,"ariaHidden":1892},[1930],"katex-html",[1840,1932,1935,1940,1999,2003,2007],{"className":1933},[1934],"base",[1840,1936],{"className":1937,"style":1939},[1938],"strut","height:0.8333em;vertical-align:-0.15em;",[1840,1941,1944,1949],{"className":1942},[1943],"mord",[1840,1945,1869],{"className":1946,"style":1948},[1943,1947],"mathnormal","margin-right:0.0813em;",[1840,1950,1953],{"className":1951},[1952],"msupsub",[1840,1954,1958,1990],{"className":1955},[1956,1957],"vlist-t","vlist-t2",[1840,1959,1962,1985],{"className":1960},[1961],"vlist-r",[1840,1963,1967],{"className":1964,"style":1966},[1965],"vlist","height:0.3011em;",[1840,1968,1970,1975],{"style":1969},"top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;",[1840,1971],{"className":1972,"style":1974},[1973],"pstrut","height:2.7em;",[1840,1976,1982],{"className":1977},[1978,1979,1980,1981],"sizing","reset-size6","size3","mtight",[1840,1983,1873],{"className":1984},[1943,1981],[1840,1986,1989],{"className":1987},[1988],"vlist-s","​",[1840,1991,1993],{"className":1992},[1961],[1840,1994,1997],{"className":1995,"style":1996},[1965],"height:0.15em;",[1840,1998],{},[1840,2000],{"className":2001,"style":2002},[1895],"margin-right:0.2778em;",[1840,2004,1877],{"className":2005},[2006],"mrel",[1840,2008],{"className":2009,"style":2002},[1895],[1840,2011,2013,2017,2020,2023,2026],{"className":2012},[1934],[1840,2014],{"className":2015,"style":2016},[1938],"height:0.625em;vertical-align:-0.1944em;",[1840,2018,1880],{"className":2019},[1943,1947],[1840,2021],{"className":2022,"style":2002},[1895],[1840,2024,1883],{"className":2025},[2006],[1840,2027],{"className":2028,"style":2002},[1895],[1840,2030,2032,2036,2077,2081,2085,2089,2129,2132,2135],{"className":2031},[1934],[1840,2033],{"className":2034,"style":2035},[1938],"height:0.8778em;vertical-align:-0.1944em;",[1840,2037,2039,2042],{"className":2038},[1943],[1840,2040,1880],{"className":2041},[1943,1947],[1840,2043,2045],{"className":2044},[1952],[1840,2046,2048,2069],{"className":2047},[1956,1957],[1840,2049,2051,2066],{"className":2050},[1961],[1840,2052,2054],{"className":2053,"style":1966},[1965],[1840,2055,2057,2060],{"style":2056},"top:-2.55em;margin-left:0em;margin-right:0.05em;",[1840,2058],{"className":2059,"style":1974},[1973],[1840,2061,2063],{"className":2062},[1978,1979,1980,1981],[1840,2064,1873],{"className":2065},[1943,1981],[1840,2067,1989],{"className":2068},[1988],[1840,2070,2072],{"className":2071},[1961],[1840,2073,2075],{"className":2074,"style":1996},[1965],[1840,2076],{},[1840,2078,1893],{"className":2079},[2080],"mpunct",[1840,2082],{"className":2083,"style":2084},[1895],"margin-right:2em;",[1840,2086],{"className":2087,"style":2088},[1895],"margin-right:0.1667em;",[1840,2090,2092,2095],{"className":2091},[1943],[1840,2093,1869],{"className":2094,"style":1948},[1943,1947],[1840,2096,2098],{"className":2097},[1952],[1840,2099,2101,2121],{"className":2100},[1956,1957],[1840,2102,2104,2118],{"className":2103},[1961],[1840,2105,2107],{"className":2106,"style":1966},[1965],[1840,2108,2109,2112],{"style":1969},[1840,2110],{"className":2111,"style":1974},[1973],[1840,2113,2115],{"className":2114},[1978,1979,1980,1981],[1840,2116,1904],{"className":2117},[1943,1981],[1840,2119,1989],{"className":2120},[1988],[1840,2122,2124],{"className":2123},[1961],[1840,2125,2127],{"className":2126,"style":1996},[1965],[1840,2128],{},[1840,2130],{"className":2131,"style":2002},[1895],[1840,2133,1877],{"className":2134},[2006],[1840,2136],{"className":2137,"style":2002},[1895],[1840,2139,2141,2145,2148,2151,2194],{"className":2140},[1934],[1840,2142],{"className":2143,"style":2144},[1938],"height:0.8889em;vertical-align:-0.1944em;",[1840,2146,1880],{"className":2147},[1943,1947],[1840,2149],{"className":2150,"style":2002},[1895],[1840,2152,2154,2187,2191],{"className":2153},[2006],[1840,2155,2157],{"className":2156},[2006],[1840,2158,2161],{"className":2159},[1943,2160],"vbox",[1840,2162,2165],{"className":2163},[2164],"thinbox",[1840,2166,2169,2172,2183],{"className":2167},[2168],"rlap",[1840,2170],{"className":2171,"style":2144},[1938],[1840,2173,2176],{"className":2174},[2175],"inner",[1840,2177,2179],{"className":2178},[1943],[1840,2180,2182],{"className":2181},[2006],"",[1840,2184],{"className":2185},[2186],"fix",[1840,2188],{"className":2189},[1895,2190],"nobreak",[1840,2192,1883],{"className":2193},[2006],[1840,2195],{"className":2196,"style":2002},[1895],[1840,2198,2200,2203,2243],{"className":2199},[1934],[1840,2201],{"className":2202,"style":2016},[1938],[1840,2204,2206,2209],{"className":2205},[1943],[1840,2207,1880],{"className":2208},[1943,1947],[1840,2210,2212],{"className":2211},[1952],[1840,2213,2215,2235],{"className":2214},[1956,1957],[1840,2216,2218,2232],{"className":2217},[1961],[1840,2219,2221],{"className":2220,"style":1966},[1965],[1840,2222,2223,2226],{"style":2056},[1840,2224],{"className":2225,"style":1974},[1973],[1840,2227,2229],{"className":2228},[1978,1979,1980,1981],[1840,2230,1873],{"className":2231},[1943,1981],[1840,2233,1989],{"className":2234},[1988],[1840,2236,2238],{"className":2237},[1961],[1840,2239,2241],{"className":2240,"style":1996},[1965],[1840,2242],{},[1840,2244,1921],{"className":2245},[1943],[1796,2247,2248],{},"必须同时说明：",[1814,2250,2251,2254,2312,2345],{},[1817,2252,2253],{},"数据模型与抽样单位；",[1817,2255,2256,2257,2311],{},"检验统计量 ",[1840,2258,2260,2285],{"className":2259},[1847],[1840,2261,2263],{"className":2262},[1851],[1853,2264,2265],{"xmlns":1855},[1858,2266,2267,2282],{},[1861,2268,2269,2272,2276,2279],{},[1867,2270,2271],{},"T",[1875,2273,2275],{"stretchy":2274},"false","(",[1867,2277,2278],{},"X",[1875,2280,2281],{"stretchy":2274},")",[1923,2283,2284],{"encoding":1925},"T(X)",[1840,2286,2288],{"className":2287,"ariaHidden":1892},[1930],[1840,2289,2291,2295,2299,2303,2307],{"className":2290},[1934],[1840,2292],{"className":2293,"style":2294},[1938],"height:1em;vertical-align:-0.25em;",[1840,2296,2271],{"className":2297,"style":2298},[1943,1947],"margin-right:0.1389em;",[1840,2300,2275],{"className":2301},[2302],"mopen",[1840,2304,2278],{"className":2305,"style":2306},[1943,1947],"margin-right:0.0785em;",[1840,2308,2281],{"className":2309},[2310],"mclose","；",[1817,2313,2314,2315,2344],{},"原假设下 ",[1840,2316,2318,2331],{"className":2317},[1847],[1840,2319,2321],{"className":2320},[1851],[1853,2322,2323],{"xmlns":1855},[1858,2324,2325,2329],{},[1861,2326,2327],{},[1867,2328,2271],{},[1923,2330,2271],{"encoding":1925},[1840,2332,2334],{"className":2333,"ariaHidden":1892},[1930],[1840,2335,2337,2341],{"className":2336},[1934],[1840,2338],{"className":2339,"style":2340},[1938],"height:0.6833em;",[1840,2342,2271],{"className":2343,"style":2298},[1943,1947]," 的分布；",[1817,2346,2347],{},"拒绝域或 p 值规则。",[1796,2349,2350],{},"双侧与单侧备择应由研究问题预先确定，不能看完效应方向再选择。",[1807,2352,2354],{"id":2353},"_2-两类错误与功效","2. 两类错误与功效",[2356,2357,2358,2513],"table",{},[2359,2360,2361],"thead",{},[2362,2363,2364,2368,2441],"tr",{},[2365,2366,2367],"th",{},"真实状态",[2365,2369,2370,2371],{},"不拒绝 ",[1840,2372,2374,2392],{"className":2373},[1847],[1840,2375,2377],{"className":2376},[1851],[1853,2378,2379],{"xmlns":1855},[1858,2380,2381,2389],{},[1861,2382,2383],{},[1864,2384,2385,2387],{},[1867,2386,1869],{},[1871,2388,1873],{},[1923,2390,2391],{"encoding":1925},"H_0",[1840,2393,2395],{"className":2394,"ariaHidden":1892},[1930],[1840,2396,2398,2401],{"className":2397},[1934],[1840,2399],{"className":2400,"style":1939},[1938],[1840,2402,2404,2407],{"className":2403},[1943],[1840,2405,1869],{"className":2406,"style":1948},[1943,1947],[1840,2408,2410],{"className":2409},[1952],[1840,2411,2413,2433],{"className":2412},[1956,1957],[1840,2414,2416,2430],{"className":2415},[1961],[1840,2417,2419],{"className":2418,"style":1966},[1965],[1840,2420,2421,2424],{"style":1969},[1840,2422],{"className":2423,"style":1974},[1973],[1840,2425,2427],{"className":2426},[1978,1979,1980,1981],[1840,2428,1873],{"className":2429},[1943,1981],[1840,2431,1989],{"className":2432},[1988],[1840,2434,2436],{"className":2435},[1961],[1840,2437,2439],{"className":2438,"style":1996},[1965],[1840,2440],{},[2365,2442,2443,2444],{},"拒绝 ",[1840,2445,2447,2464],{"className":2446},[1847],[1840,2448,2450],{"className":2449},[1851],[1853,2451,2452],{"xmlns":1855},[1858,2453,2454,2462],{},[1861,2455,2456],{},[1864,2457,2458,2460],{},[1867,2459,1869],{},[1871,2461,1873],{},[1923,2463,2391],{"encoding":1925},[1840,2465,2467],{"className":2466,"ariaHidden":1892},[1930],[1840,2468,2470,2473],{"className":2469},[1934],[1840,2471],{"className":2472,"style":1939},[1938],[1840,2474,2476,2479],{"className":2475},[1943],[1840,2477,1869],{"className":2478,"style":1948},[1943,1947],[1840,2480,2482],{"className":2481},[1952],[1840,2483,2485,2505],{"className":2484},[1956,1957],[1840,2486,2488,2502],{"className":2487},[1961],[1840,2489,2491],{"className":2490,"style":1966},[1965],[1840,2492,2493,2496],{"style":1969},[1840,2494],{"className":2495,"style":1974},[1973],[1840,2497,2499],{"className":2498},[1978,1979,1980,1981],[1840,2500,1873],{"className":2501},[1943,1981],[1840,2503,1989],{"className":2504},[1988],[1840,2506,2508],{"className":2507},[1961],[1840,2509,2511],{"className":2510,"style":1996},[1965],[1840,2512],{},[2514,2515,2516,2684],"tbody",{},[2362,2517,2518,2591,2652],{},[2519,2520,2521,2590],"td",{},[1840,2522,2524,2541],{"className":2523},[1847],[1840,2525,2527],{"className":2526},[1851],[1853,2528,2529],{"xmlns":1855},[1858,2530,2531,2539],{},[1861,2532,2533],{},[1864,2534,2535,2537],{},[1867,2536,1869],{},[1871,2538,1873],{},[1923,2540,2391],{"encoding":1925},[1840,2542,2544],{"className":2543,"ariaHidden":1892},[1930],[1840,2545,2547,2550],{"className":2546},[1934],[1840,2548],{"className":2549,"style":1939},[1938],[1840,2551,2553,2556],{"className":2552},[1943],[1840,2554,1869],{"className":2555,"style":1948},[1943,1947],[1840,2557,2559],{"className":2558},[1952],[1840,2560,2562,2582],{"className":2561},[1956,1957],[1840,2563,2565,2579],{"className":2564},[1961],[1840,2566,2568],{"className":2567,"style":1966},[1965],[1840,2569,2570,2573],{"style":1969},[1840,2571],{"className":2572,"style":1974},[1973],[1840,2574,2576],{"className":2575},[1978,1979,1980,1981],[1840,2577,1873],{"className":2578},[1943,1981],[1840,2580,1989],{"className":2581},[1988],[1840,2583,2585],{"className":2584},[1961],[1840,2586,2588],{"className":2587,"style":1996},[1965],[1840,2589],{}," 真",[2519,2592,2593,2594],{},"正确，概率 ",[1840,2595,2597,2617],{"className":2596},[1847],[1840,2598,2600],{"className":2599},[1851],[1853,2601,2602],{"xmlns":1855},[1858,2603,2604,2614],{},[1861,2605,2606,2608,2611],{},[1871,2607,1904],{},[1875,2609,2610],{},"−",[1867,2612,2613],{},"α",[1923,2615,2616],{"encoding":1925},"1-\\alpha",[1840,2618,2620,2641],{"className":2619,"ariaHidden":1892},[1930],[1840,2621,2623,2627,2630,2634,2638],{"className":2622},[1934],[1840,2624],{"className":2625,"style":2626},[1938],"height:0.7278em;vertical-align:-0.0833em;",[1840,2628,1904],{"className":2629},[1943],[1840,2631],{"className":2632,"style":2633},[1895],"margin-right:0.2222em;",[1840,2635,2610],{"className":2636},[2637],"mbin",[1840,2639],{"className":2640,"style":2633},[1895],[1840,2642,2644,2648],{"className":2643},[1934],[1840,2645],{"className":2646,"style":2647},[1938],"height:0.4306em;",[1840,2649,2613],{"className":2650,"style":2651},[1943,1947],"margin-right:0.0037em;",[2519,2653,2654,2655],{},"一类错误，概率 ",[1840,2656,2658,2672],{"className":2657},[1847],[1840,2659,2661],{"className":2660},[1851],[1853,2662,2663],{"xmlns":1855},[1858,2664,2665,2669],{},[1861,2666,2667],{},[1867,2668,2613],{},[1923,2670,2671],{"encoding":1925},"\\alpha",[1840,2673,2675],{"className":2674,"ariaHidden":1892},[1930],[1840,2676,2678,2681],{"className":2677},[1934],[1840,2679],{"className":2680,"style":2647},[1938],[1840,2682,2613],{"className":2683,"style":2651},[1943,1947],[2362,2685,2686,2758,2809],{},[2519,2687,2688,2590],{},[1840,2689,2691,2709],{"className":2690},[1847],[1840,2692,2694],{"className":2693},[1851],[1853,2695,2696],{"xmlns":1855},[1858,2697,2698,2706],{},[1861,2699,2700],{},[1864,2701,2702,2704],{},[1867,2703,1869],{},[1871,2705,1904],{},[1923,2707,2708],{"encoding":1925},"H_1",[1840,2710,2712],{"className":2711,"ariaHidden":1892},[1930],[1840,2713,2715,2718],{"className":2714},[1934],[1840,2716],{"className":2717,"style":1939},[1938],[1840,2719,2721,2724],{"className":2720},[1943],[1840,2722,1869],{"className":2723,"style":1948},[1943,1947],[1840,2725,2727],{"className":2726},[1952],[1840,2728,2730,2750],{"className":2729},[1956,1957],[1840,2731,2733,2747],{"className":2732},[1961],[1840,2734,2736],{"className":2735,"style":1966},[1965],[1840,2737,2738,2741],{"style":1969},[1840,2739],{"className":2740,"style":1974},[1973],[1840,2742,2744],{"className":2743},[1978,1979,1980,1981],[1840,2745,1904],{"className":2746},[1943,1981],[1840,2748,1989],{"className":2749},[1988],[1840,2751,2753],{"className":2752},[1961],[1840,2754,2756],{"className":2755,"style":1996},[1965],[1840,2757],{},[2519,2759,2760,2761],{},"二类错误，概率 ",[1840,2762,2764,2786],{"className":2763},[1847],[1840,2765,2767],{"className":2766},[1851],[1853,2768,2769],{"xmlns":1855},[1858,2770,2771,2783],{},[1861,2772,2773,2776,2778,2781],{},[1867,2774,2775],{},"β",[1875,2777,2275],{"stretchy":2274},[1867,2779,2780],{},"θ",[1875,2782,2281],{"stretchy":2274},[1923,2784,2785],{"encoding":1925},"\\beta(\\theta)",[1840,2787,2789],{"className":2788,"ariaHidden":1892},[1930],[1840,2790,2792,2795,2799,2802,2806],{"className":2791},[1934],[1840,2793],{"className":2794,"style":2294},[1938],[1840,2796,2775],{"className":2797,"style":2798},[1943,1947],"margin-right:0.0528em;",[1840,2800,2275],{"className":2801},[2302],[1840,2803,2780],{"className":2804,"style":2805},[1943,1947],"margin-right:0.0278em;",[1840,2807,2281],{"className":2808},[2310],[2519,2810,2811,2812],{},"功效 ",[1840,2813,2815,2839],{"className":2814},[1847],[1840,2816,2818],{"className":2817},[1851],[1853,2819,2820],{"xmlns":1855},[1858,2821,2822,2836],{},[1861,2823,2824,2826,2828,2830,2832,2834],{},[1871,2825,1904],{},[1875,2827,2610],{},[1867,2829,2775],{},[1875,2831,2275],{"stretchy":2274},[1867,2833,2780],{},[1875,2835,2281],{"stretchy":2274},[1923,2837,2838],{"encoding":1925},"1-\\beta(\\theta)",[1840,2840,2842,2860],{"className":2841,"ariaHidden":1892},[1930],[1840,2843,2845,2848,2851,2854,2857],{"className":2844},[1934],[1840,2846],{"className":2847,"style":2626},[1938],[1840,2849,1904],{"className":2850},[1943],[1840,2852],{"className":2853,"style":2633},[1895],[1840,2855,2610],{"className":2856},[2637],[1840,2858],{"className":2859,"style":2633},[1895],[1840,2861,2863,2866,2869,2872,2875],{"className":2862},[1934],[1840,2864],{"className":2865,"style":2294},[1938],[1840,2867,2775],{"className":2868,"style":2798},[1943,1947],[1840,2870,2275],{"className":2871},[2302],[1840,2873,2780],{"className":2874,"style":2805},[1943,1947],[1840,2876,2281],{"className":2877},[2310],[1796,2879,2880],{},"显著性水平控制最坏原假设下的误拒概率：",[1840,2882,2884],{"className":2883},[1843],[1840,2885,2887,2946],{"className":2886},[1847],[1840,2888,2890],{"className":2889},[1851],[1853,2891,2892],{"xmlns":1855,"display":1856},[1858,2893,2894,2943],{},[1861,2895,2896,2921,2928,2930,2934,2936,2939,2941],{},[2897,2898,2899,2907],"munder",{},[1861,2900,2901,2904],{},[1867,2902,2903],{},"sup",[1875,2905,2906],{},"⁡",[1861,2908,2909,2911,2914],{},[1867,2910,2780],{},[1875,2912,2913],{},"∈",[1864,2915,2916,2919],{},[1867,2917,2918],{"mathvariant":1911},"Θ",[1871,2920,1873],{},[1864,2922,2923,2926],{},[1867,2924,2925],{},"P",[1867,2927,2780],{},[1875,2929,2275],{"stretchy":2274},[2931,2932,2933],"mtext",{},"reject",[1875,2935,2281],{"stretchy":2274},[1875,2937,2938],{},"≤",[1867,2940,2613],{},[1867,2942,1921],{"mathvariant":1911},[1923,2944,2945],{"encoding":1925},"\\sup_{\\theta\\in\\Theta_0}P_\\theta(\\text{reject})\\le\\alpha.",[1840,2947,2949,3125],{"className":2948,"ariaHidden":1892},[1930],[1840,2950,2952,2956,3058,3061,3103,3106,3113,3116,3119,3122],{"className":2951},[1934],[1840,2953],{"className":2954,"style":2955},[1938],"height:1.7966em;vertical-align:-1.0466em;",[1840,2957,2961],{"className":2958},[2959,2960],"mop","op-limits",[1840,2962,2964,3049],{"className":2963},[1956,1957],[1840,2965,2967,3046],{"className":2966},[1961],[1840,2968,2970,3035],{"className":2969,"style":2647},[1965],[1840,2971,2973,2977],{"style":2972},"top:-2.1535em;margin-left:0em;",[1840,2974],{"className":2975,"style":2976},[1973],"height:3em;",[1840,2978,2980],{"className":2979},[1978,1979,1980,1981],[1840,2981,2983,2986,2989],{"className":2982},[1943,1981],[1840,2984,2780],{"className":2985,"style":2805},[1943,1947,1981],[1840,2987,2913],{"className":2988},[2006,1981],[1840,2990,2992,2995],{"className":2991},[1943,1981],[1840,2993,2918],{"className":2994},[1943,1981],[1840,2996,2998],{"className":2997},[1952],[1840,2999,3001,3026],{"className":3000},[1956,1957],[1840,3002,3004,3023],{"className":3003},[1961],[1840,3005,3008],{"className":3006,"style":3007},[1965],"height:0.3173em;",[1840,3009,3011,3015],{"style":3010},"top:-2.357em;margin-left:0em;margin-right:0.0714em;",[1840,3012],{"className":3013,"style":3014},[1973],"height:2.5em;",[1840,3016,3020],{"className":3017},[1978,3018,3019,1981],"reset-size3","size1",[1840,3021,1873],{"className":3022},[1943,1981],[1840,3024,1989],{"className":3025},[1988],[1840,3027,3029],{"className":3028},[1961],[1840,3030,3033],{"className":3031,"style":3032},[1965],"height:0.143em;",[1840,3034],{},[1840,3036,3038,3041],{"style":3037},"top:-3em;",[1840,3039],{"className":3040,"style":2976},[1973],[1840,3042,3043],{},[1840,3044,2903],{"className":3045},[2959],[1840,3047,1989],{"className":3048},[1988],[1840,3050,3052],{"className":3051},[1961],[1840,3053,3056],{"className":3054,"style":3055},[1965],"height:1.0466em;",[1840,3057],{},[1840,3059],{"className":3060,"style":2088},[1895],[1840,3062,3064,3067],{"className":3063},[1943],[1840,3065,2925],{"className":3066,"style":2298},[1943,1947],[1840,3068,3070],{"className":3069},[1952],[1840,3071,3073,3095],{"className":3072},[1956,1957],[1840,3074,3076,3092],{"className":3075},[1961],[1840,3077,3080],{"className":3078,"style":3079},[1965],"height:0.3361em;",[1840,3081,3083,3086],{"style":3082},"top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;",[1840,3084],{"className":3085,"style":1974},[1973],[1840,3087,3089],{"className":3088},[1978,1979,1980,1981],[1840,3090,2780],{"className":3091,"style":2805},[1943,1947,1981],[1840,3093,1989],{"className":3094},[1988],[1840,3096,3098],{"className":3097},[1961],[1840,3099,3101],{"className":3100,"style":1996},[1965],[1840,3102],{},[1840,3104,2275],{"className":3105},[2302],[1840,3107,3110],{"className":3108},[1943,3109],"text",[1840,3111,2933],{"className":3112},[1943],[1840,3114,2281],{"className":3115},[2310],[1840,3117],{"className":3118,"style":2002},[1895],[1840,3120,2938],{"className":3121},[2006],[1840,3123],{"className":3124,"style":2002},[1895],[1840,3126,3128,3131,3134],{"className":3127},[1934],[1840,3129],{"className":3130,"style":2647},[1938],[1840,3132,2613],{"className":3133,"style":2651},[1943,1947],[1840,3135,1921],{"className":3136},[1943],[1796,3138,3139],{},"功效是效应大小的函数。样本量规划必须指定最小重要效应，而不是只说“希望功效 80%”。",[1807,3141,3143],{"id":3142},"_3-p-值的准确解释","3. p 值的准确解释",[1796,3145,3146],{},"p 值是：",[1840,3148,3150],{"className":3149},[1843],[1840,3151,3153,3211],{"className":3152},[1847],[1840,3154,3156],{"className":3155},[1851],[1853,3157,3158],{"xmlns":1855,"display":1856},[1858,3159,3160,3208],{},[1861,3161,3162,3164,3166,3176,3179,3181,3183,3198,3200,3203,3206],{},[1867,3163,1796],{},[1875,3165,1883],{},[1864,3167,3168,3170],{},[1867,3169,2925],{},[1864,3171,3172,3174],{},[1867,3173,1869],{},[1871,3175,1873],{},[1875,3177,3178],{"stretchy":2274},"{",[1867,3180,2271],{},[1875,3182,2275],{"stretchy":2274},[3184,3185,3186,3188],"msup",{},[1867,3187,2278],{},[1861,3189,3190,3193,3196],{},[1867,3191,3192],{},"r",[1867,3194,3195],{},"e",[1867,3197,1796],{},[1875,3199,2281],{"stretchy":2274},[2931,3201,3202],{}," 至少与观察值同样极端",[1875,3204,3205],{"stretchy":2274},"}",[1867,3207,1921],{"mathvariant":1911},[1923,3209,3210],{"encoding":1925},"p=P_{H_0}\\{T(X^{rep})\\text{ 至少与观察值同样极端}\\}.",[1840,3212,3214,3232],{"className":3213,"ariaHidden":1892},[1930],[1840,3215,3217,3220,3223,3226,3229],{"className":3216},[1934],[1840,3218],{"className":3219,"style":2016},[1938],[1840,3221,1796],{"className":3222},[1943,1947],[1840,3224],{"className":3225,"style":2002},[1895],[1840,3227,1883],{"className":3228},[2006],[1840,3230],{"className":3231,"style":2002},[1895],[1840,3233,3235,3239,3322,3325,3328,3331,3371,3374,3386,3389],{"className":3234},[1934],[1840,3236],{"className":3237,"style":3238},[1938],"height:1.0001em;vertical-align:-0.2501em;",[1840,3240,3242,3245],{"className":3241},[1943],[1840,3243,2925],{"className":3244,"style":2298},[1943,1947],[1840,3246,3248],{"className":3247},[1952],[1840,3249,3251,3313],{"className":3250},[1956,1957],[1840,3252,3254,3310],{"className":3253},[1961],[1840,3255,3258],{"className":3256,"style":3257},[1965],"height:0.3283em;",[1840,3259,3260,3263],{"style":3082},[1840,3261],{"className":3262,"style":1974},[1973],[1840,3264,3266],{"className":3265},[1978,1979,1980,1981],[1840,3267,3269],{"className":3268},[1943,1981],[1840,3270,3272,3275],{"className":3271},[1943,1981],[1840,3273,1869],{"className":3274,"style":1948},[1943,1947,1981],[1840,3276,3278],{"className":3277},[1952],[1840,3279,3281,3302],{"className":3280},[1956,1957],[1840,3282,3284,3299],{"className":3283},[1961],[1840,3285,3287],{"className":3286,"style":3007},[1965],[1840,3288,3290,3293],{"style":3289},"top:-2.357em;margin-left:-0.0813em;margin-right:0.0714em;",[1840,3291],{"className":3292,"style":3014},[1973],[1840,3294,3296],{"className":3295},[1978,3018,3019,1981],[1840,3297,1873],{"className":3298},[1943,1981],[1840,3300,1989],{"className":3301},[1988],[1840,3303,3305],{"className":3304},[1961],[1840,3306,3308],{"className":3307,"style":3032},[1965],[1840,3309],{},[1840,3311,1989],{"className":3312},[1988],[1840,3314,3316],{"className":3315},[1961],[1840,3317,3320],{"className":3318,"style":3319},[1965],"height:0.2501em;",[1840,3321],{},[1840,3323,3178],{"className":3324},[2302],[1840,3326,2271],{"className":3327,"style":2298},[1943,1947],[1840,3329,2275],{"className":3330},[2302],[1840,3332,3334,3337],{"className":3333},[1943],[1840,3335,2278],{"className":3336,"style":2306},[1943,1947],[1840,3338,3340],{"className":3339},[1952],[1840,3341,3343],{"className":3342},[1956],[1840,3344,3346],{"className":3345},[1961],[1840,3347,3350],{"className":3348,"style":3349},[1965],"height:0.7144em;",[1840,3351,3353,3356],{"style":3352},"top:-3.113em;margin-right:0.05em;",[1840,3354],{"className":3355,"style":1974},[1973],[1840,3357,3359],{"className":3358},[1978,1979,1980,1981],[1840,3360,3362,3365,3368],{"className":3361},[1943,1981],[1840,3363,3192],{"className":3364,"style":2805},[1943,1947,1981],[1840,3366,3195],{"className":3367},[1943,1947,1981],[1840,3369,1796],{"className":3370},[1943,1947,1981],[1840,3372,2281],{"className":3373},[2310],[1840,3375,3377,3381],{"className":3376},[1943,3109],[1840,3378,3380],{"className":3379},[1943]," ",[1840,3382,3385],{"className":3383},[1943,3384],"cjk_fallback","至少与观察值同样极端",[1840,3387,3205],{"className":3388},[2310],[1840,3390,1921],{"className":3391},[1943],[1796,3393,3394],{},"它不是：",[3396,3397,3398,3526,3529,3532,3535],"ul",{},[1817,3399,3400,2311],{},[1840,3401,3403,3441],{"className":3402},[1847],[1840,3404,3406],{"className":3405},[1851],[1853,3407,3408],{"xmlns":1855},[1858,3409,3410,3438],{},[1861,3411,3412,3414,3416,3422,3425,3428,3431,3434,3436],{},[1867,3413,2925],{},[1875,3415,2275],{"stretchy":2274},[1864,3417,3418,3420],{},[1867,3419,1869],{},[1871,3421,1873],{},[1875,3423,3424],{},"∣",[1867,3426,3427],{},"d",[1867,3429,3430],{},"a",[1867,3432,3433],{},"t",[1867,3435,3430],{},[1875,3437,2281],{"stretchy":2274},[1923,3439,3440],{"encoding":1925},"P(H_0\\mid data)",[1840,3442,3444,3505],{"className":3443,"ariaHidden":1892},[1930],[1840,3445,3447,3450,3453,3456,3496,3499,3502],{"className":3446},[1934],[1840,3448],{"className":3449,"style":2294},[1938],[1840,3451,2925],{"className":3452,"style":2298},[1943,1947],[1840,3454,2275],{"className":3455},[2302],[1840,3457,3459,3462],{"className":3458},[1943],[1840,3460,1869],{"className":3461,"style":1948},[1943,1947],[1840,3463,3465],{"className":3464},[1952],[1840,3466,3468,3488],{"className":3467},[1956,1957],[1840,3469,3471,3485],{"className":3470},[1961],[1840,3472,3474],{"className":3473,"style":1966},[1965],[1840,3475,3476,3479],{"style":1969},[1840,3477],{"className":3478,"style":1974},[1973],[1840,3480,3482],{"className":3481},[1978,1979,1980,1981],[1840,3483,1873],{"className":3484},[1943,1981],[1840,3486,1989],{"className":3487},[1988],[1840,3489,3491],{"className":3490},[1961],[1840,3492,3494],{"className":3493,"style":1996},[1965],[1840,3495],{},[1840,3497],{"className":3498,"style":2002},[1895],[1840,3500,3424],{"className":3501},[2006],[1840,3503],{"className":3504,"style":2002},[1895],[1840,3506,3508,3511,3514,3517,3520,3523],{"className":3507},[1934],[1840,3509],{"className":3510,"style":2294},[1938],[1840,3512,3427],{"className":3513},[1943,1947],[1840,3515,3430],{"className":3516},[1943,1947],[1840,3518,3433],{"className":3519},[1943,1947],[1840,3521,3430],{"className":3522},[1943,1947],[1840,3524,2281],{"className":3525},[2310],[1817,3527,3528],{},"“结果由随机造成的概率”；",[1817,3530,3531],{},"效应为零的概率；",[1817,3533,3534],{},"未来重复研究成功的概率；",[1817,3536,3537],{},"效应大小或实际重要性。",[1796,3539,3540,3541,3610],{},"p 值小表示数据与指定 ",[1840,3542,3544,3561],{"className":3543},[1847],[1840,3545,3547],{"className":3546},[1851],[1853,3548,3549],{"xmlns":1855},[1858,3550,3551,3559],{},[1861,3552,3553],{},[1864,3554,3555,3557],{},[1867,3556,1869],{},[1871,3558,1873],{},[1923,3560,2391],{"encoding":1925},[1840,3562,3564],{"className":3563,"ariaHidden":1892},[1930],[1840,3565,3567,3570],{"className":3566},[1934],[1840,3568],{"className":3569,"style":1939},[1938],[1840,3571,3573,3576],{"className":3572},[1943],[1840,3574,1869],{"className":3575,"style":1948},[1943,1947],[1840,3577,3579],{"className":3578},[1952],[1840,3580,3582,3602],{"className":3581},[1956,1957],[1840,3583,3585,3599],{"className":3584},[1961],[1840,3586,3588],{"className":3587,"style":1966},[1965],[1840,3589,3590,3593],{"style":1969},[1840,3591],{"className":3592,"style":1974},[1973],[1840,3594,3596],{"className":3595},[1978,1979,1980,1981],[1840,3597,1873],{"className":3598},[1943,1981],[1840,3600,1989],{"className":3601},[1988],[1840,3603,3605],{"className":3604},[1961],[1840,3606,3608],{"className":3607,"style":1996},[1965],[1840,3609],{}," 及其模型较不相容。若模型、停止规则或分析选择错误，数值也会失去校准。",[1807,3612,3614],{"id":3613},"_4-neymanpearson-与似然比","4. Neyman–Pearson 与似然比",[1796,3616,3617,3618,3646],{},"对于简单原假设和简单备择，Neyman–Pearson 引理说明：在固定 ",[1840,3619,3621,3634],{"className":3620},[1847],[1840,3622,3624],{"className":3623},[1851],[1853,3625,3626],{"xmlns":1855},[1858,3627,3628,3632],{},[1861,3629,3630],{},[1867,3631,2613],{},[1923,3633,2671],{"encoding":1925},[1840,3635,3637],{"className":3636,"ariaHidden":1892},[1930],[1840,3638,3640,3643],{"className":3639},[1934],[1840,3641],{"className":3642,"style":2647},[1938],[1840,3644,2613],{"className":3645,"style":2651},[1943,1947]," 下，拒绝较大似然比",[1840,3648,3650],{"className":3649},[1843],[1840,3651,3653,3707],{"className":3652},[1847],[1840,3654,3656],{"className":3655},[1851],[1853,3657,3658],{"xmlns":1855,"display":1856},[1858,3659,3660,3704],{},[1861,3661,3662],{},[3663,3664,3665,3686],"mfrac",{},[1861,3666,3667,3670,3672,3678,3681,3684],{},[1867,3668,3669],{},"L",[1875,3671,2275],{"stretchy":2274},[1864,3673,3674,3676],{},[1867,3675,2780],{},[1871,3677,1904],{},[1875,3679,3680],{"separator":1892},";",[1867,3682,3683],{},"x",[1875,3685,2281],{"stretchy":2274},[1861,3687,3688,3690,3692,3698,3700,3702],{},[1867,3689,3669],{},[1875,3691,2275],{"stretchy":2274},[1864,3693,3694,3696],{},[1867,3695,2780],{},[1871,3697,1873],{},[1875,3699,3680],{"separator":1892},[1867,3701,3683],{},[1875,3703,2281],{"stretchy":2274},[1923,3705,3706],{"encoding":1925},"\\frac{L(\\theta_1;x)}{L(\\theta_0;x)}",[1840,3708,3710],{"className":3709,"ariaHidden":1892},[1930],[1840,3711,3713,3717],{"className":3712},[1934],[1840,3714],{"className":3715,"style":3716},[1938],"height:2.363em;vertical-align:-0.936em;",[1840,3718,3720,3724,3895],{"className":3719},[1943],[1840,3721],{"className":3722},[2302,3723],"nulldelimiter",[1840,3725,3727],{"className":3726},[3663],[1840,3728,3730,3886],{"className":3729},[1956,1957],[1840,3731,3733,3883],{"className":3732},[1961],[1840,3734,3737,3805,3816],{"className":3735,"style":3736},[1965],"height:1.427em;",[1840,3738,3740,3743],{"style":3739},"top:-2.314em;",[1840,3741],{"className":3742,"style":2976},[1973],[1840,3744,3746,3749,3752,3793,3796,3799,3802],{"className":3745},[1943],[1840,3747,3669],{"className":3748},[1943,1947],[1840,3750,2275],{"className":3751},[2302],[1840,3753,3755,3758],{"className":3754},[1943],[1840,3756,2780],{"className":3757,"style":2805},[1943,1947],[1840,3759,3761],{"className":3760},[1952],[1840,3762,3764,3785],{"className":3763},[1956,1957],[1840,3765,3767,3782],{"className":3766},[1961],[1840,3768,3770],{"className":3769,"style":1966},[1965],[1840,3771,3773,3776],{"style":3772},"top:-2.55em;margin-left:-0.0278em;margin-right:0.05em;",[1840,3774],{"className":3775,"style":1974},[1973],[1840,3777,3779],{"className":3778},[1978,1979,1980,1981],[1840,3780,1873],{"className":3781},[1943,1981],[1840,3783,1989],{"className":3784},[1988],[1840,3786,3788],{"className":3787},[1961],[1840,3789,3791],{"className":3790,"style":1996},[1965],[1840,3792],{},[1840,3794,3680],{"className":3795},[2080],[1840,3797],{"className":3798,"style":2088},[1895],[1840,3800,3683],{"className":3801},[1943,1947],[1840,3803,2281],{"className":3804},[2310],[1840,3806,3808,3811],{"style":3807},"top:-3.23em;",[1840,3809],{"className":3810,"style":2976},[1973],[1840,3812],{"className":3813,"style":3815},[3814],"frac-line","border-bottom-width:0.04em;",[1840,3817,3819,3822],{"style":3818},"top:-3.677em;",[1840,3820],{"className":3821,"style":2976},[1973],[1840,3823,3825,3828,3831,3871,3874,3877,3880],{"className":3824},[1943],[1840,3826,3669],{"className":3827},[1943,1947],[1840,3829,2275],{"className":3830},[2302],[1840,3832,3834,3837],{"className":3833},[1943],[1840,3835,2780],{"className":3836,"style":2805},[1943,1947],[1840,3838,3840],{"className":3839},[1952],[1840,3841,3843,3863],{"className":3842},[1956,1957],[1840,3844,3846,3860],{"className":3845},[1961],[1840,3847,3849],{"className":3848,"style":1966},[1965],[1840,3850,3851,3854],{"style":3772},[1840,3852],{"className":3853,"style":1974},[1973],[1840,3855,3857],{"className":3856},[1978,1979,1980,1981],[1840,3858,1904],{"className":3859},[1943,1981],[1840,3861,1989],{"className":3862},[1988],[1840,3864,3866],{"className":3865},[1961],[1840,3867,3869],{"className":3868,"style":1996},[1965],[1840,3870],{},[1840,3872,3680],{"className":3873},[2080],[1840,3875],{"className":3876,"style":2088},[1895],[1840,3878,3683],{"className":3879},[1943,1947],[1840,3881,2281],{"className":3882},[2310],[1840,3884,1989],{"className":3885},[1988],[1840,3887,3889],{"className":3888},[1961],[1840,3890,3893],{"className":3891,"style":3892},[1965],"height:0.936em;",[1840,3894],{},[1840,3896],{"className":3897},[2310,3723],[1796,3899,3900],{},"的检验最有力。",[1796,3902,3903],{},"复合假设常使用广义似然比：",[1840,3905,3907],{"className":3906},[1843],[1840,3908,3910,4001],{"className":3909},[1847],[1840,3911,3913],{"className":3912},[1851],[1853,3914,3915],{"xmlns":1855,"display":1856},[1858,3916,3917,3998],{},[1861,3918,3919,3922,3924,3926,3928,3930,3996],{},[1867,3920,3921],{"mathvariant":1911},"Λ",[1875,3923,2275],{"stretchy":2274},[1867,3925,3683],{},[1875,3927,2281],{"stretchy":2274},[1875,3929,1883],{},[3663,3931,3932,3966],{},[1861,3933,3934,3954,3956,3958,3960,3962,3964],{},[2897,3935,3936,3942],{},[1861,3937,3938,3940],{},[1867,3939,2903],{},[1875,3941,2906],{},[1861,3943,3944,3946,3948],{},[1867,3945,2780],{},[1875,3947,2913],{},[1864,3949,3950,3952],{},[1867,3951,2918],{"mathvariant":1911},[1871,3953,1873],{},[1867,3955,3669],{},[1875,3957,2275],{"stretchy":2274},[1867,3959,2780],{},[1875,3961,3680],{"separator":1892},[1867,3963,3683],{},[1875,3965,2281],{"stretchy":2274},[1861,3967,3968,3984,3986,3988,3990,3992,3994],{},[2897,3969,3970,3976],{},[1861,3971,3972,3974],{},[1867,3973,2903],{},[1875,3975,2906],{},[1861,3977,3978,3980,3982],{},[1867,3979,2780],{},[1875,3981,2913],{},[1867,3983,2918],{"mathvariant":1911},[1867,3985,3669],{},[1875,3987,2275],{"stretchy":2274},[1867,3989,2780],{},[1875,3991,3680],{"separator":1892},[1867,3993,3683],{},[1875,3995,2281],{"stretchy":2274},[1867,3997,1921],{"mathvariant":1911},[1923,3999,4000],{"encoding":1925},"\\Lambda(x)\n=\n\\frac{\\sup_{\\theta\\in\\Theta_0}L(\\theta;x)}\n{\\sup_{\\theta\\in\\Theta}L(\\theta;x)}.",[1840,4002,4004,4031],{"className":4003,"ariaHidden":1892},[1930],[1840,4005,4007,4010,4013,4016,4019,4022,4025,4028],{"className":4006},[1934],[1840,4008],{"className":4009,"style":2294},[1938],[1840,4011,3921],{"className":4012},[1943],[1840,4014,2275],{"className":4015},[2302],[1840,4017,3683],{"className":4018},[1943,1947],[1840,4020,2281],{"className":4021},[2310],[1840,4023],{"className":4024,"style":2002},[1895],[1840,4026,1883],{"className":4027},[2006],[1840,4029],{"className":4030,"style":2002},[1895],[1840,4032,4034,4038,4284],{"className":4033},[1934],[1840,4035],{"className":4036,"style":4037},[1938],"height:2.4418em;vertical-align:-0.9575em;",[1840,4039,4041,4044,4281],{"className":4040},[1943],[1840,4042],{"className":4043},[2302,3723],[1840,4045,4047],{"className":4046},[3663],[1840,4048,4050,4272],{"className":4049},[1956,1957],[1840,4051,4053,4269],{"className":4052},[1961],[1840,4054,4057,4141,4149],{"className":4055,"style":4056},[1965],"height:1.4842em;",[1840,4058,4059,4062],{"style":3739},[1840,4060],{"className":4061,"style":2976},[1973],[1840,4063,4065,4117,4120,4123,4126,4129,4132,4135,4138],{"className":4064},[1943],[1840,4066,4068,4071],{"className":4067},[2959],[1840,4069,2903],{"className":4070},[2959],[1840,4072,4074],{"className":4073},[1952],[1840,4075,4077,4108],{"className":4076},[1956,1957],[1840,4078,4080,4105],{"className":4079},[1961],[1840,4081,4084],{"className":4082,"style":4083},[1965],"height:0.242em;",[1840,4085,4087,4090],{"style":4086},"top:-2.4559em;margin-right:0.05em;",[1840,4088],{"className":4089,"style":1974},[1973],[1840,4091,4093],{"className":4092},[1978,1979,1980,1981],[1840,4094,4096,4099,4102],{"className":4095},[1943,1981],[1840,4097,2780],{"className":4098,"style":2805},[1943,1947,1981],[1840,4100,2913],{"className":4101},[2006,1981],[1840,4103,2918],{"className":4104},[1943,1981],[1840,4106,1989],{"className":4107},[1988],[1840,4109,4111],{"className":4110},[1961],[1840,4112,4115],{"className":4113,"style":4114},[1965],"height:0.2715em;",[1840,4116],{},[1840,4118],{"className":4119,"style":2088},[1895],[1840,4121,3669],{"className":4122},[1943,1947],[1840,4124,2275],{"className":4125},[2302],[1840,4127,2780],{"className":4128,"style":2805},[1943,1947],[1840,4130,3680],{"className":4131},[2080],[1840,4133],{"className":4134,"style":2088},[1895],[1840,4136,3683],{"className":4137},[1943,1947],[1840,4139,2281],{"className":4140},[2310],[1840,4142,4143,4146],{"style":3807},[1840,4144],{"className":4145,"style":2976},[1973],[1840,4147],{"className":4148,"style":3815},[3814],[1840,4150,4152,4155],{"style":4151},"top:-3.7342em;",[1840,4153],{"className":4154,"style":2976},[1973],[1840,4156,4158,4245,4248,4251,4254,4257,4260,4263,4266],{"className":4157},[1943],[1840,4159,4161,4164],{"className":4160},[2959],[1840,4162,2903],{"className":4163},[2959],[1840,4165,4167],{"className":4166},[1952],[1840,4168,4170,4236],{"className":4169},[1956,1957],[1840,4171,4173,4233],{"className":4172},[1961],[1840,4174,4176],{"className":4175,"style":4083},[1965],[1840,4177,4178,4181],{"style":4086},[1840,4179],{"className":4180,"style":1974},[1973],[1840,4182,4184],{"className":4183},[1978,1979,1980,1981],[1840,4185,4187,4190,4193],{"className":4186},[1943,1981],[1840,4188,2780],{"className":4189,"style":2805},[1943,1947,1981],[1840,4191,2913],{"className":4192},[2006,1981],[1840,4194,4196,4199],{"className":4195},[1943,1981],[1840,4197,2918],{"className":4198},[1943,1981],[1840,4200,4202],{"className":4201},[1952],[1840,4203,4205,4225],{"className":4204},[1956,1957],[1840,4206,4208,4222],{"className":4207},[1961],[1840,4209,4211],{"className":4210,"style":3007},[1965],[1840,4212,4213,4216],{"style":3010},[1840,4214],{"className":4215,"style":3014},[1973],[1840,4217,4219],{"className":4218},[1978,3018,3019,1981],[1840,4220,1873],{"className":4221},[1943,1981],[1840,4223,1989],{"className":4224},[1988],[1840,4226,4228],{"className":4227},[1961],[1840,4229,4231],{"className":4230,"style":3032},[1965],[1840,4232],{},[1840,4234,1989],{"className":4235},[1988],[1840,4237,4239],{"className":4238},[1961],[1840,4240,4243],{"className":4241,"style":4242},[1965],"height:0.3442em;",[1840,4244],{},[1840,4246],{"className":4247,"style":2088},[1895],[1840,4249,3669],{"className":4250},[1943,1947],[1840,4252,2275],{"className":4253},[2302],[1840,4255,2780],{"className":4256,"style":2805},[1943,1947],[1840,4258,3680],{"className":4259},[2080],[1840,4261],{"className":4262,"style":2088},[1895],[1840,4264,3683],{"className":4265},[1943,1947],[1840,4267,2281],{"className":4268},[2310],[1840,4270,1989],{"className":4271},[1988],[1840,4273,4275],{"className":4274},[1961],[1840,4276,4279],{"className":4277,"style":4278},[1965],"height:0.9575em;",[1840,4280],{},[1840,4282],{"className":4283},[2310,3723],[1840,4285,1921],{"className":4286},[1943],[1796,4288,4289,4290,4349],{},"正则条件下，",[1840,4291,4293,4317],{"className":4292},[1847],[1840,4294,4296],{"className":4295},[1851],[1853,4297,4298],{"xmlns":1855},[1858,4299,4300,4314],{},[1861,4301,4302,4304,4307,4310,4312],{},[1875,4303,2610],{},[1871,4305,4306],{},"2",[1867,4308,4309],{},"log",[1875,4311,2906],{},[1867,4313,3921],{"mathvariant":1911},[1923,4315,4316],{"encoding":1925},"-2\\log\\Lambda",[1840,4318,4320],{"className":4319,"ariaHidden":1892},[1930],[1840,4321,4323,4326,4329,4332,4335,4343,4346],{"className":4322},[1934],[1840,4324],{"className":4325,"style":2144},[1938],[1840,4327,2610],{"className":4328},[1943],[1840,4330,4306],{"className":4331},[1943],[1840,4333],{"className":4334,"style":2088},[1895],[1840,4336,4338,4339],{"className":4337},[2959],"lo",[1840,4340,4342],{"style":4341},"margin-right:0.0139em;","g",[1840,4344],{"className":4345,"style":2088},[1895],[1840,4347,3921],{"className":4348},[1943]," 常渐近服从卡方；边界参数、混合模型和弱识别可产生非标准极限。",[1807,4351,4353],{"id":4352},"_5-可运行案例样本量效应与功效","5. 可运行案例：样本量、效应与功效",[1796,4355,4356,4357,4423],{},"已知标准差为 1，双侧 z 检验使用 ",[1840,4358,4360,4385],{"className":4359},[1847],[1840,4361,4363],{"className":4362},[1851],[1853,4364,4365],{"xmlns":1855},[1858,4366,4367,4382],{},[1861,4368,4369,4371,4374,4376,4379],{},[1867,4370,3424],{"mathvariant":1911},[1867,4372,4373],{},"Z",[1867,4375,3424],{"mathvariant":1911},[1875,4377,4378],{},">",[1871,4380,4381],{},"1.96",[1923,4383,4384],{"encoding":1925},"|Z|>1.96",[1840,4386,4388,4413],{"className":4387,"ariaHidden":1892},[1930],[1840,4389,4391,4394,4397,4401,4404,4407,4410],{"className":4390},[1934],[1840,4392],{"className":4393,"style":2294},[1938],[1840,4395,3424],{"className":4396},[1943],[1840,4398,4373],{"className":4399,"style":4400},[1943,1947],"margin-right:0.0715em;",[1840,4402,3424],{"className":4403},[1943],[1840,4405],{"className":4406,"style":2002},[1895],[1840,4408,4378],{"className":4409},[2006],[1840,4411],{"className":4412,"style":2002},[1895],[1840,4414,4416,4420],{"className":4415},[1934],[1840,4417],{"className":4418,"style":4419},[1938],"height:0.6444em;",[1840,4421,4381],{"className":4422},[1943],"。代码模拟不同真实效应和样本量。",[4425,4426],"pyodide",{"code64":4427,"layout":4428,"locale":7,"packages":4429,"title":4430},"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","vertical","numpy","Python：一类错误与功效曲线",[1796,4432,4433],{},"效应为 0 时拒绝率接近 0.05；效应固定时样本量增加提高功效。大样本也会让极小效应显著，因此必须同时报告估计值、区间和实际阈值。",[1807,4435,4437],{"id":4436},"_6-区间与检验的对偶","6. 区间与检验的对偶",[1796,4439,4440,4441,4469,4470,4520,4521,4592,4593,4749],{},"对双侧水平 ",[1840,4442,4444,4457],{"className":4443},[1847],[1840,4445,4447],{"className":4446},[1851],[1853,4448,4449],{"xmlns":1855},[1858,4450,4451,4455],{},[1861,4452,4453],{},[1867,4454,2613],{},[1923,4456,2671],{"encoding":1925},[1840,4458,4460],{"className":4459,"ariaHidden":1892},[1930],[1840,4461,4463,4466],{"className":4462},[1934],[1840,4464],{"className":4465,"style":2647},[1938],[1840,4467,2613],{"className":4468,"style":2651},[1943,1947]," 检验，若 ",[1840,4471,4473,4490],{"className":4472},[1847],[1840,4474,4476],{"className":4475},[1851],[1853,4477,4478],{"xmlns":1855},[1858,4479,4480,4488],{},[1861,4481,4482,4484,4486],{},[1871,4483,1904],{},[1875,4485,2610],{},[1867,4487,2613],{},[1923,4489,2616],{"encoding":1925},[1840,4491,4493,4511],{"className":4492,"ariaHidden":1892},[1930],[1840,4494,4496,4499,4502,4505,4508],{"className":4495},[1934],[1840,4497],{"className":4498,"style":2626},[1938],[1840,4500,1904],{"className":4501},[1943],[1840,4503],{"className":4504,"style":2633},[1895],[1840,4506,2610],{"className":4507},[2637],[1840,4509],{"className":4510,"style":2633},[1895],[1840,4512,4514,4517],{"className":4513},[1934],[1840,4515],{"className":4516,"style":2647},[1938],[1840,4518,2613],{"className":4519,"style":2651},[1943,1947]," 置信区间不包含 ",[1840,4522,4524,4542],{"className":4523},[1847],[1840,4525,4527],{"className":4526},[1851],[1853,4528,4529],{"xmlns":1855},[1858,4530,4531,4539],{},[1861,4532,4533],{},[1864,4534,4535,4537],{},[1867,4536,2780],{},[1871,4538,1873],{},[1923,4540,4541],{"encoding":1925},"\\theta_0",[1840,4543,4545],{"className":4544,"ariaHidden":1892},[1930],[1840,4546,4548,4552],{"className":4547},[1934],[1840,4549],{"className":4550,"style":4551},[1938],"height:0.8444em;vertical-align:-0.15em;",[1840,4553,4555,4558],{"className":4554},[1943],[1840,4556,2780],{"className":4557,"style":2805},[1943,1947],[1840,4559,4561],{"className":4560},[1952],[1840,4562,4564,4584],{"className":4563},[1956,1957],[1840,4565,4567,4581],{"className":4566},[1961],[1840,4568,4570],{"className":4569,"style":1966},[1965],[1840,4571,4572,4575],{"style":3772},[1840,4573],{"className":4574,"style":1974},[1973],[1840,4576,4578],{"className":4577},[1978,1979,1980,1981],[1840,4579,1873],{"className":4580},[1943,1981],[1840,4582,1989],{"className":4583},[1988],[1840,4585,4587],{"className":4586},[1961],[1840,4588,4590],{"className":4589,"style":1996},[1965],[1840,4591],{},"，通常等价于拒绝 ",[1840,4594,4596,4626],{"className":4595},[1847],[1840,4597,4599],{"className":4598},[1851],[1853,4600,4601],{"xmlns":1855},[1858,4602,4603,4623],{},[1861,4604,4605,4611,4613,4615,4617],{},[1864,4606,4607,4609],{},[1867,4608,1869],{},[1871,4610,1873],{},[1875,4612,1877],{},[1867,4614,2780],{},[1875,4616,1883],{},[1864,4618,4619,4621],{},[1867,4620,2780],{},[1871,4622,1873],{},[1923,4624,4625],{"encoding":1925},"H_0:\\theta=\\theta_0",[1840,4627,4629,4684,4703],{"className":4628,"ariaHidden":1892},[1930],[1840,4630,4632,4635,4675,4678,4681],{"className":4631},[1934],[1840,4633],{"className":4634,"style":1939},[1938],[1840,4636,4638,4641],{"className":4637},[1943],[1840,4639,1869],{"className":4640,"style":1948},[1943,1947],[1840,4642,4644],{"className":4643},[1952],[1840,4645,4647,4667],{"className":4646},[1956,1957],[1840,4648,4650,4664],{"className":4649},[1961],[1840,4651,4653],{"className":4652,"style":1966},[1965],[1840,4654,4655,4658],{"style":1969},[1840,4656],{"className":4657,"style":1974},[1973],[1840,4659,4661],{"className":4660},[1978,1979,1980,1981],[1840,4662,1873],{"className":4663},[1943,1981],[1840,4665,1989],{"className":4666},[1988],[1840,4668,4670],{"className":4669},[1961],[1840,4671,4673],{"className":4672,"style":1996},[1965],[1840,4674],{},[1840,4676],{"className":4677,"style":2002},[1895],[1840,4679,1877],{"className":4680},[2006],[1840,4682],{"className":4683,"style":2002},[1895],[1840,4685,4687,4691,4694,4697,4700],{"className":4686},[1934],[1840,4688],{"className":4689,"style":4690},[1938],"height:0.6944em;",[1840,4692,2780],{"className":4693,"style":2805},[1943,1947],[1840,4695],{"className":4696,"style":2002},[1895],[1840,4698,1883],{"className":4699},[2006],[1840,4701],{"className":4702,"style":2002},[1895],[1840,4704,4706,4709],{"className":4705},[1934],[1840,4707],{"className":4708,"style":4551},[1938],[1840,4710,4712,4715],{"className":4711},[1943],[1840,4713,2780],{"className":4714,"style":2805},[1943,1947],[1840,4716,4718],{"className":4717},[1952],[1840,4719,4721,4741],{"className":4720},[1956,1957],[1840,4722,4724,4738],{"className":4723},[1961],[1840,4725,4727],{"className":4726,"style":1966},[1965],[1840,4728,4729,4732],{"style":3772},[1840,4730],{"className":4731,"style":1974},[1973],[1840,4733,4735],{"className":4734},[1978,1979,1980,1981],[1840,4736,1873],{"className":4737},[1943,1981],[1840,4739,1989],{"className":4740},[1988],[1840,4742,4744],{"className":4743},[1961],[1840,4745,4747],{"className":4746,"style":1996},[1965],[1840,4748],{},"。但前提是区间和检验使用同一模型、尾部与近似。",[1796,4751,4752],{},"区间提供与数据相容的参数范围，比一个二元“显著\u002F不显著”包含更多信息。",[1807,4754,4756],{"id":4755},"_7-可选停止","7. 可选停止",[1796,4758,4759,4760,4814],{},"固定样本 p 值假设样本量与停止规则预先确定。若每天查看一次并在首次 ",[1840,4761,4763,4783],{"className":4762},[1847],[1840,4764,4766],{"className":4765},[1851],[1853,4767,4768],{"xmlns":1855},[1858,4769,4770,4780],{},[1861,4771,4772,4774,4777],{},[1867,4773,1796],{},[1875,4775,4776],{},"\u003C",[1871,4778,4779],{},"0.05",[1923,4781,4782],{"encoding":1925},"p\u003C0.05",[1840,4784,4786,4805],{"className":4785,"ariaHidden":1892},[1930],[1840,4787,4789,4793,4796,4799,4802],{"className":4788},[1934],[1840,4790],{"className":4791,"style":4792},[1938],"height:0.7335em;vertical-align:-0.1944em;",[1840,4794,1796],{"className":4795},[1943,1947],[1840,4797],{"className":4798,"style":2002},[1895],[1840,4800,4776],{"className":4801},[2006],[1840,4803],{"className":4804,"style":2002},[1895],[1840,4806,4808,4811],{"className":4807},[1934],[1840,4809],{"className":4810,"style":4419},[1938],[1840,4812,4779],{"className":4813},[1943]," 时停止，一类错误通常膨胀。",[1796,4816,4817],{},"可选方案：",[3396,4819,4820,4823,4826,4829],{},[1817,4821,4822],{},"预先固定样本量；",[1817,4824,4825],{},"群序贯设计与 alpha spending；",[1817,4827,4828],{},"随时有效 p 值、e-process 或置信序列；",[1817,4830,4831],{},"将所有中期查看纳入正式方案。",[1796,4833,4834],{},"前沿章会用 Ville 不等式解释随时有效推断。",[1807,4836,4838],{"id":4837},"_8-多重检验","8. 多重检验",[1796,4840,4841,4842,4871],{},"进行 ",[1840,4843,4845,4859],{"className":4844},[1847],[1840,4846,4848],{"className":4847},[1851],[1853,4849,4850],{"xmlns":1855},[1858,4851,4852,4857],{},[1861,4853,4854],{},[1867,4855,4856],{},"m",[1923,4858,4856],{"encoding":1925},[1840,4860,4862],{"className":4861,"ariaHidden":1892},[1930],[1840,4863,4865,4868],{"className":4864},[1934],[1840,4866],{"className":4867,"style":2647},[1938],[1840,4869,4856],{"className":4870},[1943,1947]," 个独立的 5% 检验时，至少一次误拒概率为：",[1840,4873,4875],{"className":4874},[1843],[1840,4876,4878,4910],{"className":4877},[1847],[1840,4879,4881],{"className":4880},[1851],[1853,4882,4883],{"xmlns":1855,"display":1856},[1858,4884,4885,4907],{},[1861,4886,4887,4889,4891,4893,4895,4897,4899,4905],{},[1871,4888,1904],{},[1875,4890,2610],{},[1875,4892,2275],{"stretchy":2274},[1871,4894,1904],{},[1875,4896,2610],{},[1871,4898,4779],{},[3184,4900,4901,4903],{},[1875,4902,2281],{"stretchy":2274},[1867,4904,4856],{},[1867,4906,1921],{"mathvariant":1911},[1923,4908,4909],{"encoding":1925},"1-(1-0.05)^m.",[1840,4911,4913,4931,4952],{"className":4912,"ariaHidden":1892},[1930],[1840,4914,4916,4919,4922,4925,4928],{"className":4915},[1934],[1840,4917],{"className":4918,"style":2626},[1938],[1840,4920,1904],{"className":4921},[1943],[1840,4923],{"className":4924,"style":2633},[1895],[1840,4926,2610],{"className":4927},[2637],[1840,4929],{"className":4930,"style":2633},[1895],[1840,4932,4934,4937,4940,4943,4946,4949],{"className":4933},[1934],[1840,4935],{"className":4936,"style":2294},[1938],[1840,4938,2275],{"className":4939},[2302],[1840,4941,1904],{"className":4942},[1943],[1840,4944],{"className":4945,"style":2633},[1895],[1840,4947,2610],{"className":4948},[2637],[1840,4950],{"className":4951,"style":2633},[1895],[1840,4953,4955,4958,4961,4990],{"className":4954},[1934],[1840,4956],{"className":4957,"style":2294},[1938],[1840,4959,4779],{"className":4960},[1943],[1840,4962,4964,4967],{"className":4963},[2310],[1840,4965,2281],{"className":4966},[2310],[1840,4968,4970],{"className":4969},[1952],[1840,4971,4973],{"className":4972},[1956],[1840,4974,4976],{"className":4975},[1961],[1840,4977,4979],{"className":4978,"style":3349},[1965],[1840,4980,4981,4984],{"style":3352},[1840,4982],{"className":4983,"style":1974},[1973],[1840,4985,4987],{"className":4986},[1978,1979,1980,1981],[1840,4988,4856],{"className":4989},[1943,1947,1981],[1840,4991,1921],{"className":4992},[1943],[1796,4994,4995],{},"常见目标不同：",[3396,4997,4998,5001,5004],{},[1817,4999,5000],{},"FWER：至少一个假阳性的概率；",[1817,5002,5003],{},"FDR：拒绝结果中假阳性的期望比例；",[1817,5005,5006],{},"simultaneous coverage：一组区间同时覆盖。",[1796,5008,5009],{},"Bonferroni 控制 FWER；Benjamini–Hochberg 在相应依赖条件下控制 FDR。选择方法要先写清错误目标。",[1807,5011,5013],{"id":5012},"_9-诊断清单","9. 诊断清单",[3396,5015,5016,5019,5022,5095,5098,5101,5104],{},[1817,5017,5018],{},"原假设和备择是否在看数据前写清？",[1817,5020,5021],{},"单侧方向是否有制度依据？",[1817,5023,5024,5025,5094],{},"检验统计量在 ",[1840,5026,5028,5045],{"className":5027},[1847],[1840,5029,5031],{"className":5030},[1851],[1853,5032,5033],{"xmlns":1855},[1858,5034,5035,5043],{},[1861,5036,5037],{},[1864,5038,5039,5041],{},[1867,5040,1869],{},[1871,5042,1873],{},[1923,5044,2391],{"encoding":1925},[1840,5046,5048],{"className":5047,"ariaHidden":1892},[1930],[1840,5049,5051,5054],{"className":5050},[1934],[1840,5052],{"className":5053,"style":1939},[1938],[1840,5055,5057,5060],{"className":5056},[1943],[1840,5058,1869],{"className":5059,"style":1948},[1943,1947],[1840,5061,5063],{"className":5062},[1952],[1840,5064,5066,5086],{"className":5065},[1956,1957],[1840,5067,5069,5083],{"className":5068},[1961],[1840,5070,5072],{"className":5071,"style":1966},[1965],[1840,5073,5074,5077],{"style":1969},[1840,5075],{"className":5076,"style":1974},[1973],[1840,5078,5080],{"className":5079},[1978,1979,1980,1981],[1840,5081,1873],{"className":5082},[1943,1981],[1840,5084,1989],{"className":5085},[1988],[1840,5087,5089],{"className":5088},[1961],[1840,5090,5092],{"className":5091,"style":1996},[1965],[1840,5093],{}," 下如何校准？",[1817,5096,5097],{},"样本量是否按最小重要效应规划？",[1817,5099,5100],{},"是否查看、筛选或停止过多次？",[1817,5102,5103],{},"是否进行了多个终点、亚组或模型检验？",[1817,5105,5106],{},"是否同时报告效应、区间、功效和假设？",[1807,5108,5109],{"id":5109},"常见误区",[3396,5111,5112,5115,5170,5173,5176],{},[1817,5113,5114],{},"“不显著”写成“证明没有效应”；",[1817,5116,5117,5169],{},[1840,5118,5120,5139],{"className":5119},[1847],[1840,5121,5123],{"className":5122},[1851],[1853,5124,5125],{"xmlns":1855},[1858,5126,5127,5136],{},[1861,5128,5129,5131,5133],{},[1867,5130,1796],{},[1875,5132,1883],{},[1871,5134,5135],{},"0.03",[1923,5137,5138],{"encoding":1925},"p=0.03",[1840,5140,5142,5160],{"className":5141,"ariaHidden":1892},[1930],[1840,5143,5145,5148,5151,5154,5157],{"className":5144},[1934],[1840,5146],{"className":5147,"style":2016},[1938],[1840,5149,1796],{"className":5150},[1943,1947],[1840,5152],{"className":5153,"style":2002},[1895],[1840,5155,1883],{"className":5156},[2006],[1840,5158],{"className":5159,"style":2002},[1895],[1840,5161,5163,5166],{"className":5162},[1934],[1840,5164],{"className":5165,"style":4419},[1938],[1840,5167,5135],{"className":5168},[1943]," 写成“原假设只有 3% 概率为真”；",[1817,5171,5172],{},"两组一个显著、一个不显著，就称组间差异显著；",[1817,5174,5175],{},"看完结果后改单双侧；",[1817,5177,5178],{},"把统计显著等同于临床或经济重要。",[1807,5180,5181],{"id":5181},"课堂任务",[1796,5183,5184],{},"为“新教学方法提高平均成绩”写一份检验方案：",[1814,5186,5187,5190,5318,5350,5353],{},[1817,5188,5189],{},"定义最小重要差异；",[1817,5191,5192,5193,5317],{},"写 ",[1840,5194,5196,5222],{"className":5195},[1847],[1840,5197,5199],{"className":5198},[1851],[1853,5200,5201],{"xmlns":1855},[1858,5202,5203,5219],{},[1861,5204,5205,5211,5213],{},[1864,5206,5207,5209],{},[1867,5208,1869],{},[1871,5210,1873],{},[1875,5212,1893],{"separator":1892},[1864,5214,5215,5217],{},[1867,5216,1869],{},[1871,5218,1904],{},[1923,5220,5221],{"encoding":1925},"H_0,H_1",[1840,5223,5225],{"className":5224,"ariaHidden":1892},[1930],[1840,5226,5228,5231,5271,5274,5277],{"className":5227},[1934],[1840,5229],{"className":5230,"style":2035},[1938],[1840,5232,5234,5237],{"className":5233},[1943],[1840,5235,1869],{"className":5236,"style":1948},[1943,1947],[1840,5238,5240],{"className":5239},[1952],[1840,5241,5243,5263],{"className":5242},[1956,1957],[1840,5244,5246,5260],{"className":5245},[1961],[1840,5247,5249],{"className":5248,"style":1966},[1965],[1840,5250,5251,5254],{"style":1969},[1840,5252],{"className":5253,"style":1974},[1973],[1840,5255,5257],{"className":5256},[1978,1979,1980,1981],[1840,5258,1873],{"className":5259},[1943,1981],[1840,5261,1989],{"className":5262},[1988],[1840,5264,5266],{"className":5265},[1961],[1840,5267,5269],{"className":5268,"style":1996},[1965],[1840,5270],{},[1840,5272,1893],{"className":5273},[2080],[1840,5275],{"className":5276,"style":2088},[1895],[1840,5278,5280,5283],{"className":5279},[1943],[1840,5281,1869],{"className":5282,"style":1948},[1943,1947],[1840,5284,5286],{"className":5285},[1952],[1840,5287,5289,5309],{"className":5288},[1956,1957],[1840,5290,5292,5306],{"className":5291},[1961],[1840,5293,5295],{"className":5294,"style":1966},[1965],[1840,5296,5297,5300],{"style":1969},[1840,5298],{"className":5299,"style":1974},[1973],[1840,5301,5303],{"className":5302},[1978,1979,1980,1981],[1840,5304,1904],{"className":5305},[1943,1981],[1840,5307,1989],{"className":5308},[1988],[1840,5310,5312],{"className":5311},[1961],[1840,5313,5315],{"className":5314,"style":1996},[1965],[1840,5316],{}," 与单双侧理由；",[1817,5319,5320,5321,5349],{},"指定 ",[1840,5322,5324,5337],{"className":5323},[1847],[1840,5325,5327],{"className":5326},[1851],[1853,5328,5329],{"xmlns":1855},[1858,5330,5331,5335],{},[1861,5332,5333],{},[1867,5334,2613],{},[1923,5336,2671],{"encoding":1925},[1840,5338,5340],{"className":5339,"ariaHidden":1892},[1930],[1840,5341,5343,5346],{"className":5342},[1934],[1840,5344],{"className":5345,"style":2647},[1938],[1840,5347,2613],{"className":5348,"style":2651},[1943,1947],"、目标功效与样本量输入；",[1817,5351,5352],{},"说明期中查看如何处理；",[1817,5354,5355],{},"列出主要终点与多重检验规则。",[1807,5357,5358],{"id":5358},"核心阅读",[3396,5360,5361,5369,5378],{},[1817,5362,5363,5364,5368],{},"Lehmann & Romano, ",[5365,5366,5367],"em",{},"Testing Statistical Hypotheses","。",[1817,5370,5371,5372,5368],{},"Wasserstein & Lazar (2016), ",[3430,5373,5377],{"href":5374,"rel":5375},"https:\u002F\u002Fdoi.org\u002F10.1080\u002F00031305.2016.1154108",[5376],"nofollow","“The ASA Statement on p-Values”",[1817,5379,5380,5381,5368],{},"Wasserstein, Schirm & Lazar (2019), ",[3430,5382,5385],{"href":5383,"rel":5384},"https:\u002F\u002Fdoi.org\u002F10.1080\u002F00031305.2019.1583913",[5376],"“Moving to a World Beyond p \u003C 0.05”",[1796,5387,5388,5389,5393,5394,5368],{},"上一章：",[3430,5390,5392],{"href":5391},"..\u002F04-pe-method\u002F","点估计方法","｜下一章：",[3430,5395,5397],{"href":5396},"..\u002F06-hypothesis-method\u002F","常用检验方法",{"title":10,"searchDepth":5399,"depth":5399,"links":5400},2,[5401,5402,5403,5404,5405,5406,5407,5408,5409,5410,5411,5412,5413],{"id":1809,"depth":5399,"text":1809},{"id":1834,"depth":5399,"text":1835},{"id":2353,"depth":5399,"text":2354},{"id":3142,"depth":5399,"text":3143},{"id":3613,"depth":5399,"text":3614},{"id":4352,"depth":5399,"text":4353},{"id":4436,"depth":5399,"text":4437},{"id":4755,"depth":5399,"text":4756},{"id":4837,"depth":5399,"text":4838},{"id":5012,"depth":5399,"text":5013},{"id":5109,"depth":5399,"text":5109},{"id":5181,"depth":5399,"text":5181},{"id":5358,"depth":5399,"text":5358},"从错误概率、p 值与功效出发，理解 Neyman–Pearson、似然比、可选停止和多重检验。","md",{"sidebar":5417},{"order":5418},11,true,{"title":1710,"description":5414},"R8N_DfG7S9tvsSDbKA_VS5SmbxpJ4FVAgpTwo9jiBMk",[5423,5425],{"title":1704,"path":1705,"stem":1706,"description":5424,"children":-1},"比较矩估计、极大似然、贝叶斯估计和 EM 算法的构造逻辑、计算与边界。",{"title":1716,"path":1717,"stem":1718,"description":5426,"children":-1},"按研究设计选择 t、比例、卡方、ANOVA、秩和与置换检验，并报告效应与假设。",1785754757398]