{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp04_retrieval_first_paired20_20260802","cell_id":"negative_space_design__behavioral_economics","hypothesis_id":"H5","search_queries":["missing data visualization decision making distinguish zero no data dashboard study","regulatory oversight dashboard missing reports zero incidents data quality guidance","cause specific empty state no data delayed permission error dashboard design","patent dashboard missing data cause notification recommended action","site:gov.uk dashboard \"no data\" \"zero\" missing official statistics","site:who.int missing data distinguish zero reporting data quality dashboard","monitoring system nonreporting alert follow up missing report dashboard","data absent reason recommended action decision support standard missing observation"],"sources":[{"source_id":"H5-C1","title":"Data Quality Assurance: Module 2—Desk Review of Data Quality","publisher":"World Health Organization","url":"https://cdn.who.int/media/docs/default-source/data-quality-pages/2021_-dqa_module-2_desk-review-of-data-quality.pdf","source_class":"OFFICIAL_GUIDANCE","claims_supported":["Missing data must be differentiated from true zero values because zero denotes no reportable events while missingness denotes unreported events.","The guidance separately measures missing values and zeros, identifies low-completeness entities, and assigns interpretation to data and programme managers."]},{"source_id":"H5-C2","title":"Observation - FHIR v4.0.1: Element Definitions","publisher":"Health Level Seven International","url":"https://hl7.org/fhir/R4/observation-definitions.html","source_class":"STANDARD","claims_supported":["FHIR provides dataAbsentReason to encode why an expected observation value is missing.","The standard separates actual values from absence reasons such as error and requires use-case agreements for interpreting exceptional values."]},{"source_id":"H5-C3","title":"CDASH Implementation Guide v2.0","publisher":"Clinical Data Interchange Standards Consortium","url":"https://www.cdisc.org/standards/foundational/cdash/cdashig-v2-0","source_class":"STANDARD","claims_supported":["CDASH states that an absent response is ambiguous between no, none, and missing and can delay identification of serious events.","It recommends explicit not-performed indicators and prompts confirming that blank records are intentional, partly to determine whether a data query is needed."]},{"source_id":"H5-C4","title":"Empty state","publisher":"UK Intelligence Community Design System","url":"https://design.sis.gov.uk/components/feedback-progress/empty-state/","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","claims_supported":["The design system distinguishes empty-state causes including first use, failed loading, no results, removed data, and access restrictions.","It directs designers to explain what happened, why it happened, and what to do next, while limiting the number of actions."]},{"source_id":"H5-C5","title":"Empty States","publisher":"SAP Fiori Design System","url":"https://www.sap.com/design-system/fiori-design-web/v1-120/foundations/best-practices/global-patterns/designing-for-empty-states","source_class":"COMMERCIAL_FIRST_PARTY","claims_supported":["SAP distinguishes no-data, user-action, and error states, with errors further attributed to permissions, configuration, or system problems.","Each state receives different guidance, including context-appropriate next steps or corrective actions."]},{"source_id":"H5-C6","title":"Understanding the Effects of Visualizing Missing Values on Visual Data Exploration","publisher":"IEEE VIS","url":"https://virtual.ieeevis.org/year/2021/paper_v-short-1160.html","source_class":"PRIMARY_RESEARCH","claims_supported":["A controlled study found that displaying otherwise-missing records using estimated values and uncertainty marks changed participants' decision workflows compared with omitting those records.","The study supports the behavioral premise that representation of missingness affects decisions, although it did not test oversight case closure or cause-specific actions."]},{"source_id":"H5-C7","title":"Exposure Monitoring Quick Start Guide v1.44","publisher":"MITRE / Sara Alert","url":"https://saraalert.org/wp-content/uploads/2022/04/ExposureMonitoring_1.44_QuickStart.pdf","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","claims_supported":["Sara Alert separates non-reporting people from asymptomatic and symptomatic people rather than interpreting missing reports as absence of symptoms.","Its monitoring workflow gives public-health users state-specific actions such as adding an obtained report, logging a contact attempt, documenting reasoning, or changing investigation status."]},{"source_id":"H5-C8","title":"Regulatory guidance: Dashboards","publisher":"Office for Statistics Regulation","url":"https://osr.statisticsauthority.gov.uk/guidance/regulatory-guidance-dashboards/pages/6/","source_class":"GOVERNMENT_OR_REGULATOR","claims_supported":["Official-statistics dashboards should expose data limitations, uncertainty, errors, update timing, and information critical to preventing misinterpretation.","Dashboard content and maintenance should be tied to identified user questions and needs."]}],"proximity":"ESTABLISHED_PRACTICE","closest_analogues":[{"name":"Cause-specific, action-bearing empty states in government and enterprise design systems","similarity":"The UK Intelligence Community and SAP guidance already prescribe diagnosing materially different empty-state causes, explaining the cause, and presenting a context-specific next or corrective action.","remaining_difference":"They provide general interface practice rather than evidence that the pattern changes case-closure and follow-up behavior among regulators facing strategic under-reporting.","source_ids":["H5-C4","H5-C5"]},{"name":"WHO missing-versus-zero data-quality review","similarity":"WHO directly addresses institutional monitoring reports, rejects treating blanks as zero incidents, identifies deficient entity-periods, and assigns managerial interpretation.","remaining_difference":"It does not present a dashboard experiment comparing cause-action pairs with an equally salient generic missing-data warning.","source_ids":["H5-C1","H5-C8"]},{"name":"Sara Alert non-reporting workflow","similarity":"A deployed public-health monitoring product classifies non-reporting separately from no symptoms and connects that state to follow-up and documentation actions.","remaining_difference":"It covers one principal absence cause in public-health monitoring rather than several diagnosed causes across general regulatory oversight, and no comparative behavioral evaluation was located.","source_ids":["H5-C7"]},{"name":"FHIR and CDASH explicit absence semantics","similarity":"These standards encode why expected data are absent and use explicit completeness prompts to prevent blanks from being misread as negative findings.","remaining_difference":"They operate mainly at data representation and collection stages, not at the downstream oversight-dashboard decision point.","source_ids":["H5-C2","H5-C3"]}],"overlapping_components":["Explicit separation of true zero or no event from missing or unreported data","Cause coding for absent observations","Distinct empty states for unavailable, failed, filtered, permission-blocked, and genuinely empty data","Cause-appropriate next or corrective action","Non-reporting status as an operational monitoring category","Follow-up and reasoning documentation","Preservation of data-quality and update context","Behavioral salience of missingness"],"remaining_contrastive_claim":"In regulator-like entity-period decisions, pairing each diagnosed absence cause with one prescribed oversight action reduces false no-incident closure more than an equally salient generic missing-data warning.","claim_falsifier":"The claim would be falsified by a pre-existing controlled evaluation of the same cause-label-plus-matched-action treatment in oversight decisions, or by a well-powered comparison showing no reduction in false closure or appropriate follow-up relative to a generic missingness warning.","problem_support":"STRONG","recommendation":"DIFFUSION_LANE","world_novelty_boundary":"Within eight ordinary-web queries and eight opened sources, explicit missing-versus-zero handling, cause-specific empty-state diagnosis, and matched next actions were already established across standards, official guidance, design systems, and a public-health monitoring product; no directly relevant patent surfaced, and no source established the narrower comparative behavioral effect in regulator-like case-closure decisions."}