Human-Capacity Accommodation Design

batch: Batch 18

canonical_family: Human-Centered Appraisal & Design

slug: accommodation

slug: affordance

slug: observability

slug: equity

slug: consent

slug: agency

slug: autonomy

slug: representation

components: Essential Outcome, User, and Context Frame

components: Representative Human-Capacity and Variability Profile

components: Task, Environment, and Interface Demand Map

components: Mismatch, Barrier, Workaround, and Risk Ledger

components: Accommodation Option and Equivalent-Path Set

components: Safety, Dignity, Privacy, and Burden Gate

components: Accommodation Implementation, Support, and Ownership Plan

components: In-Context Fit, Breakdown, and Revision Monitor

mechanisms: Essential-Function and Method-Separation Review

mechanisms: Multimodal Equivalence and Assistive-Compatibility Test

mechanisms: Accommodation Failure and Recovery Rehearsal

mechanisms: Longitudinal Fit, Equity, and Burden Audit

essence_one_line: Diagnose the mismatch between human capacity and system demand, then change the task, environment, interface, timing, modality, or support so people can achieve essential outcomes safely and with dignity.

compression_statement: Systems often encode an imagined standard user and treat deviations in sensory, motor, cognitive, linguistic, emotional, health, age, fatigue, stress, or situational capacity as personal failure. Human-Capacity Accommodation Design defines essential outcomes separately from inherited methods; maps representative capability ranges and fluctuation; analyzes person-task-environment mismatch; identifies preventable demand and barriers; co-designs equivalent alternatives, adjustments, assistive interfaces, pacing, recovery, and support; checks safety, privacy, stigma, workload transfer, and feasibility; implements with consent and accountable ownership; validates in real contexts; monitors breakdown, drift, maintenance, and inequity; and revises without forcing repeated disclosure.

core_intuition: The person is not the defective component. Capability and demand interact, and either side of the interface can change. Preserve the essential outcome, not an inherited method that excludes users.

canonical_formula: essential_outcome_and_context + representative_capacity_envelope + demand_and_barrier_map + mismatch_and_risk_analysis + co_designed_accommodation_options + equivalence_safety_dignity_gate + implementation_and_support + in_use_validation_and_revision -> durable_human_system_fit

structural_problem: A task or environment requires sensory, motor, cognitive, linguistic, temporal, emotional, or physiological capacities that users do not consistently have. The system hides this mismatch by selecting only successful users, relying on workarounds, demanding disclosure, or shifting effort to families and support staff.

trigger_conditions: People fail, avoid, or abandon a task despite being able to achieve its essential outcome through another method.

trigger_conditions: Performance degrades under fatigue, stress, interruption, disability, language, aging, illness, or temporary constraint.

trigger_conditions: One modality, posture, pace, location, schedule, device, or communication channel is mandatory without necessity.

trigger_conditions: Errors are predictable consequences of demand, interface, or environment rather than lack of motivation.

trigger_conditions: Support exists informally but is stigmatized, unreliable, or invisible in capacity planning.

symptoms: Accessible mode lagging behind primary mode

root_tension: Common defaults reduce complexity and cost, but one fixed method excludes variable humans. Highly individualized accommodation improves fit but can create delay, privacy burden, fragmentation, stigmatization, and maintenance failure. The pattern combines inclusive defaults with governed personalization.

anti_signatures: The task is only to test assumptions with users; use User Context Validation.

anti_signatures: Only unnecessary information load must be reduced; use Cognitive Load Reduction.

anti_signatures: A barrier can be removed for everyone without preserving an essential constraint; use ordinary simplification or redesign.

anti_signatures: The requested change would remove a genuinely essential safety or outcome requirement without an equivalent control.

structural_intervention: Establish a participatory accommodation lifecycle that separates essential outcomes from incidental methods, maps human capacity and system demand, generates equivalent alternatives, evaluates risk and dignity, implements with support and ownership, and validates durable fit in context.

action_logic: Define users, affected nonusers, context, task, essential outcome, safety duties, and inherited methods.

action_logic: Gather representative evidence through consent-based observation, self-report, performance, incidents, and support-worker knowledge.

action_logic: Map capability ranges and fluctuation across sensory, motor, cognitive, linguistic, emotional, health, fatigue, and situational dimensions.

action_logic: Decompose task and environment demands by modality, force, reach, memory, attention, pace, interruption, posture, language, social exposure, and recovery.

action_logic: Identify mismatch, barrier, workaround, exclusion, error, injury, privacy, stigma, and transferred workload.

action_logic: Remove nonessential demands and generate inclusive defaults, equivalent modalities, adjustable pacing, assistive compatibility, and human support.

action_logic: Compare options by outcome equivalence, safety, autonomy, privacy, effort, delay, reliability, compatibility, and burden transfer.

action_logic: Co-select and document accommodation with the person; minimize disclosure and provide appeal and rapid repair.

action_logic: Assign implementation, procurement, training, maintenance, backup, handoff, and funding ownership.

action_logic: Validate under real context and capacity variation; monitor use, breakdown, disparity, drift, and changing needs and revise.

decision_rules: Preserve essential outcomes and safety functions while challenging inherited methods and convenience requirements.

decision_rules: Prefer inclusive defaults when they help broad users without material harm; preserve personalization where needs conflict.

decision_rules: Do not require diagnosis or identity disclosure beyond what is necessary to select and support an accommodation.

decision_rules: Treat user effort, delay, stigma, privacy loss, and support-worker burden as system costs.

decision_rules: An alternative is equivalent only if it supports the same meaningful outcome, agency, timeliness, and remedy.

decision_rules: Provide backup and rapid repair when accommodation failure blocks essential participation or service.

target_invariants: Essential outcome, inherited method, and safety constraint are distinguished explicitly.

target_invariants: Capability evidence represents variation and fluctuation, not an average or ideal user alone.

target_invariants: People affected by the design have meaningful choice and can contest mismatch assumptions.

target_invariants: Accommodation does not silently transfer unreasonable burden to another person or group.

target_invariants: Privacy, dignity, autonomy, and nonretaliation are preserved.

target_invariants: Assistive and alternate modes receive equivalent testing, maintenance, and change control.

target_invariants: Real-context validation and repair remain available after implementation.

expected_outcomes: More resilient performance under capacity fluctuation

prose_essence:
Diagnose mismatch between human capacity and system demand, then change the task, environment, interface, timing, modality, or support so people can achieve essential outcomes safely and with dignity. Preserve the outcome, not an inherited method that excludes users.

The unit of analysis is fit, not the isolated person. A capacity can be sufficient in one setting and overwhelmed in another because the required rate, duration, precision, sensory channel, interruption tolerance, recovery time, or social exposure changed. Conversely, a demanding task can become workable when its sequencing, interface, tool, environment, timing, or assistance changes. This relational view prevents the design team from treating every difficulty as a deficit located inside the user.

Accommodation is therefore a governed transformation of demand. It may remove a nonessential requirement, distribute effort across time, substitute an equivalent modality, introduce an assistive bridge, change the physical environment, supply human support, or create a protected recovery path. The transformation is successful only when the person can reach the meaningful outcome with comparable agency, timeliness, safety, privacy, and remedy—and when the path remains operable after the pilot ends.

prose_when_to_use:
Use it when sensory, motor, cognitive, linguistic, emotional, health, fatigue, or situational limits interact with task or environmental demands; when workarounds and support are hidden; or when alternate methods can preserve the same meaningful outcome.

Use User Context Validation when only assumptions need testing, Cognitive Load Reduction for information burden alone, and ordinary simplification when a barrier can be removed for everyone without accommodation governance.

### Strong application signals

The pattern is especially useful when performance varies sharply across apparently similar people or across the same person's day; when successful completion depends on undocumented assistance; when people avoid a channel before entering the measured funnel; when a nominally optional method is practically mandatory; or when an accessibility exception exists but requires delay, repeated proof, public disclosure, or exceptional persistence. These signals point to a capacity-demand relation rather than a simple knowledge gap.

Use the full lifecycle when the accommodation has dependencies that can decay: configured technology, trained staff, an interpreter roster, a modified schedule, a physical device, a synchronized alternate workflow, or a manager decision. A one-time adjustment may be enough for a temporary low-risk task, but a recurring or safety-relevant accommodation needs ownership, fallback, maintenance, and revision.

The archetype also applies before an individual requests help. Population-level evidence can reveal predictable exclusion and support inclusive defaults. Individual choice remains necessary because people with similar diagnoses or labels can have different capacities, strategies, preferences, risks, and privacy boundaries.

prose_structural_problem:
Systems encode an imagined standard user and misclassify predictable mismatch as low motivation or inability. Fixed modality, pace, posture, language, location, or disclosure requirements exclude capable people, create error and injury, and transfer effort to users and support networks.

### Diagnose demand before prescribing support

A mature diagnosis separates at least five layers. The **essential outcome** is the result that must be achieved. The **task method** is the current sequence used to reach it. The **demand profile** describes what that sequence asks of perception, movement, memory, attention, language, endurance, emotional regulation, and time. The **capacity envelope** describes what a person or population can reliably supply under relevant conditions. The **consequence map** records what happens when demand exceeds capacity: error, pain, abandonment, unsafe work, delayed service, stigma, disclosure, or transferred labor.

Demand is multidimensional and cumulative. A form may be readable but still inaccessible because it times out; a workstation may be reachable but not sustainable for a full shift; a meeting may provide captions but combine rapid turn-taking, ambiguous agendas, sensory load, and no recovery interval. Profile peak demand, sustained demand, switching cost, simultaneous demand, and recovery debt—not merely whether each isolated action is possible once.

Capacity is likewise a range rather than a fixed score. It changes with fatigue, medication, pain, stress, environmental noise, acute illness, sleep, competing caregiving, unfamiliarity, and prior effort. Design against the reliable operating envelope and relevant worst-reasonable conditions, while keeping enough adjustability for individual strategy. Designing to a population average systematically hides tails, intersections, and fluctuation.

Evidence must include absent and unsuccessful users. Completion data describe survivors. Workarounds, abandoned attempts, requests routed elsewhere, family assistance, staff rescue, injury reports, and nonparticipation often reveal the actual mismatch. The diagnostic question is not only “Can a person complete this?” but “At what effort, risk, delay, dependency, and probability of recovery?”

prose_intervention_logic:
Separate essential outcomes and safety constraints from inherited methods. Map representative capacity ranges and fluctuation, decompose task and environmental demands, and link mismatch to barriers and harm. Co-design inclusive defaults and equivalent paths; test outcome, safety, dignity, privacy, effort, delay, assistive compatibility, and burden transfer. Implement with ownership, maintenance, backup, and repair, then validate in real contexts and revise.

### 1. Frame the outcome and authority

Write the essential outcome in method-neutral terms and identify who has authority to challenge each requirement. Trace safety claims to evidence, not tradition. A requirement such as “respond within five seconds,” “appear in person,” “lift this object manually,” or “speak without assistance” may be essential, partially essential, or merely inherited. Record the reason, consequence of relaxation, and available compensating controls.

### 2. Build capacity and demand profiles

Profile relevant capacity dimensions only; do not collect diagnostic detail because it is available. For each task segment, record modality, force or dexterity, precision, reach, posture, memory load, attention duration, switching frequency, language complexity, emotional exposure, social coordination, pace, duration, recovery opportunity, and environmental conditions. Mark which demands are adjustable, substitutable, sequence-dependent, or coupled to other demands.

### 3. Locate and prioritize mismatches

Cross the profiles and rank mismatches by severity, frequency, irreversibility, inequity, and effect on the essential outcome. Distinguish a barrier from a coping strategy: a colleague completing the inaccessible step is evidence of mismatch, not proof that the process works. Prioritize bottlenecks that block entry, create injury, force disclosure, or make later recovery impossible.

### 4. Transform demand and generate options

Start with removal of nonessential demand, then consider redistribution, substitution, augmentation, assistance, and recovery. Combine inclusive defaults with personal configuration. A strong option set includes more than one feasible path when needs can conflict: quiet space may help one person while remote participation helps another; slower pacing may improve accuracy while creating fatigue for someone else. Co-design exposes these interactions before a single “accessible solution” becomes another fixed standard.

### 5. Gate for meaningful equivalence

Compare options using the same outcome and consequence measures as the primary path. Test safety, completion, quality, time, effort, privacy, independence, social exposure, access to appeal, and reliability. The alternative need not be identical, but it must not impose a material penalty unrelated to the essential outcome. When perfect equivalence is impossible, make the residual difference explicit, obtain informed choice where appropriate, and assign authority to mitigate or review it.

### 6. Operationalize, rehearse, and revise

Treat configuration, procurement, training, scheduling, staffing, funding, documentation, handoff, maintenance, security, backup, repair, and change synchronization as part of the design. Rehearse failures before dependence becomes critical. Validate in actual contexts and under capacity variation, then feed outcome, effort, incidents, abandonment, support burden, and repair time into a revision loop. The lifecycle closes only when detected mismatch can trigger a funded change.

prose_key_components:
The outcome and context frame defines what matters. The capacity profile represents human variation. The demand map locates system requirements. The mismatch ledger links them to consequences. The option set generates alternatives. The safety and dignity gate tests equivalence. The implementation plan sustains delivery. The fit monitor detects breakdown and change.

### Essential outcome, user, and context frame

This frame prevents method lock-in. It identifies the person or population, the setting, the meaningful result, true safety constraints, affected third parties, decision authority, and the methods currently treated as mandatory. It should explicitly name excluded nonusers and downstream recipients of transferred work. Without this frame, teams optimize the existing procedure and call the optimization accommodation.

### Representative capacity and variability profile

The profile describes functional ranges in context rather than inferring needs from identity labels. It includes fluctuation, compensating strategies, cumulative fatigue, recovery, and intersections among capacities. Its detail should be proportional to the decision: enough to choose and test an accommodation, no more than necessary for privacy and governance.

### Task, environment, and interface demand map

The demand map decomposes the system side of the relation. It records peak and sustained loads, simultaneity, transitions, interruption, precision, timing, environment, and social requirements. It also shows where one demand amplifies another—for example, time pressure can increase memory load and error cost, while noise can increase listening effort and fatigue.

### Mismatch, barrier, workaround, and risk ledger

The ledger joins evidence to consequence. Each entry states the demand, affected capacity range, context, observed or credible consequence, existing workaround, burden bearer, uncertainty, and priority. Keeping assumptions separate from observations lets users contest deficit-based interpretations and helps reviewers see where more evidence is needed.

### Accommodation option and equivalent-path set

The option set spans universal changes, configurable defaults, individual adjustments, assistive technologies, environmental changes, human support, and fallback. Options are composable: adjustable timing alone may not help if error recovery remains inaccessible. The set preserves choice and records why rejected options failed equivalence, safety, feasibility, or user-preference tests.

### Safety, dignity, privacy, and burden gate

This gate stops cheap but inferior alternatives from passing as accommodations. It evaluates risk controls, agency, disclosure, stigma, time, effort, quality, independence, remedy, and burden transfer. Decisions are documented and contestable, especially when cost or operational convenience is weighed against exclusion.

### Implementation, support, and ownership plan

Approval without delivery is not accommodation. The plan names owners, dates, funding, dependencies, configuration, training, support hours, handoffs, spares, repair targets, and synchronization with the primary system. It keeps the user from becoming the unpaid program manager for their own access.

### In-context fit, breakdown, and revision monitor

The monitor observes whether the path works over time without turning into surveillance. It tracks meaningful outcomes, effort, errors, abandonment, failure, repair, disparity, and support load at the minimum granularity needed. Findings connect to a revision owner and response threshold; otherwise monitoring merely documents harm.

prose_common_mechanisms:
Person–task–environment analysis locates mismatch. Essential-function review challenges inherited methods. Participatory workshops generate options. Multimodal and assistive tests establish equivalence. Failure rehearsals test backup and repair. Longitudinal audits reveal disparity, decay, and shifted burden.

These mechanisms implement different stages and should not be mistaken for the parent pattern. A mismatch analysis can produce a correct diagnosis but no funded change. A workshop can generate attractive options without safety evidence. An automated accessibility scan can find some technical defects while missing timing, cognitive effort, social exposure, and recovery. The archetype coordinates their outputs into a durable accommodation lifecycle.

Select mechanisms according to uncertainty and consequence. Use direct contextual observation where workarounds hide demand; structured task analysis where sequencing and concurrency matter; participatory option generation where preferences and conflicting needs are central; instrumented testing where timing, force, error, or compatibility requires measurement; and longitudinal audit where adaptation, drift, or burden transfer may appear slowly. High-risk changes need independent safety review and rehearsed fallback in addition to user validation.

Mechanisms should accept qualitative evidence. A person's report of pain, overload, stigma, or unsustainable effort is not invalid because the system lacks a sensor for it. Triangulate self-report, observation, outcome data, incidents, and support-worker knowledge while avoiding demands for repeated proof. Evidence thresholds should rise with the risk of the proposed change, not with skepticism toward the claimant.

prose_failure_modes:
Average-user design excludes nonusers. Inherited methods masquerade as essential functions. Accommodation becomes extra coordination work. Burden shifts to families or staff. Alternate modes decay. Supposed equivalence loses time, agency, privacy, or remedy. Representative evidence, essential-function review, explicit ownership, burden measurement, parity testing, and contextual validation mitigate these failures.

### Diagnostic failures

Teams may use a diagnosis as a proxy for capacity, test only current successful users, observe a task in ideal conditions, or isolate each demand while missing cumulative load. Detect this when the same label yields inconsistent outcomes, when field performance is worse than laboratory results, or when support and recovery time disappear from the record. Rebuild the profile with contextual ranges, nonusers, peak conditions, and workaround evidence.

### Selection failures

A team may choose the first requested tool without exploring system-side changes, optimize cost rather than meaningful equivalence, or impose a “helpful” restriction that removes agency. Detect this through low adoption, persistent workarounds, longer completion, dependence, or user refusal. Reopen the option set, clarify preferences, compare alternatives on outcome and burden, and preserve the right to decline paternalistic solutions.

### Implementation failures

Approved accommodations can stall in procurement, arrive without configuration or training, conflict with security policy, fail during handoff, or depend on one knowledgeable employee. Detect missed dates, repeated escalations, configuration drift, and user coordination effort. Give one accountable owner authority across dependencies, publish service targets, maintain configuration records and spares, and test handoffs.

### Operational and change failures

Primary systems evolve while alternate paths lag; staffing changes erase informal knowledge; assistive tools update; or a person's capacity and context change. Detect parity defects, rising error, unexplained abandonment, repair recurrence, and divergence after releases. Put alternate paths inside ordinary change control, regression testing, incident review, and lifecycle budgeting.

### Recovery failures

A nominal backup may require the same unavailable capacity as the failed path, expose private information, or ask the user to coordinate multiple teams during an outage. Recovery design should specify an accessible notification, a genuinely independent fallback, restoration of state and preferences, escalation authority, maximum outage, and post-incident correction. Rehearse credible combinations such as tool failure plus unavailable specialist support, not only single-component failures.

prose_neighbor_distinctions:
User Context Validation tests assumptions; this pattern redesigns and sustains fit. Cognitive Load Reduction addresses one demand class. Equity Adjustment addresses distributional outcomes. Sociotechnical Integration coordinates subsystems. Physical Constraint Design prevents prohibited action. Human-Capacity Accommodation Design uniquely governs person–task–environment mismatch and equivalent access.

It is also distinct from generic simplification. Simplification removes unnecessary complexity from a task for anyone. Accommodation begins with a specific or representative capacity-demand mismatch and may preserve complexity while changing modality, pacing, sequencing, support, or environment. If plain language or fewer steps fully solves the problem without differentiated paths or ongoing governance, ordinary redesign is enough.

It is not training. Training changes a person's knowledge, skill, or strategy. Accommodation changes the conditions under which existing capability can produce the outcome. Training can be one support mechanism, but it is inappropriate when the demand is unnecessary, the capacity is not trainable within the relevant horizon, or the burden of adaptation should lie with the system. Repeatedly coaching a person around a preventable barrier is workaround institutionalization.

It is not paternalistic restriction. Removing choice, narrowing responsibility, or routing someone into a segregated lower-opportunity path may reduce immediate risk while also reducing autonomy and development. Legitimate safety constraints require evidence, proportionality, exploration of alternative controls, affected-person participation, and review. Protection without those controls is not accommodation design.

Universal design is a close strategy, not a complete synonym. Inclusive defaults can prevent many mismatches before individual disclosure, while accommodation governs residual, conflicting, fluctuating, or uncommon needs and the operational path that sustains them. Ergonomics, accessibility testing, assistive technology, flexible scheduling, and interpretation are mechanisms or domain forms; none alone supplies the entire diagnosis-to-revision structure.

prose_cross_domain_examples:
A digital service can offer equivalent screen-reader, keyboard, and adjustable-time workflows. A workplace can preserve skilled output through workstation and schedule changes. A public service can provide text, interpretation, asynchronous preparation, and private support rather than one spoken appointment channel.

In education, the essential outcome may be demonstrating analysis rather than handwriting speed or performance in a single timed modality. A capacity-demand analysis can support alternative response modes, bounded extra time, low-distraction settings, accessible materials, assistive compatibility, and equivalent feedback while preserving construct validity and academic standards.

In healthcare, a care pathway can combine accessible communication, lower sensory demand, supported decision-making, flexible appointment length, mobility-compatible facilities, and a documented fallback when interpretation or equipment fails. The design measures clinical outcome, consent, privacy, delay, and caregiver burden rather than merely counting offered services.

In transportation, equivalent trip access requires more than a nominal accessible vehicle. Booking, arrival information, boarding, securement, disruption notice, re-routing, assistance, and complaint resolution must all work under real conditions. Monitoring includes missed trips, extra wait, failure recovery, injury, and dependence on informal help.

In emergency operations, capacity can degrade for everyone through smoke, noise, stress, injury, fatigue, protective equipment, or communication failure. Multimodal alerts, simpler critical choices, accessible evacuation support, redundant channels, protected rest, and rehearsed assistance show how accommodation logic improves population resilience without assuming a permanently labeled user group.

prose_non_examples:
User interviews without redesign authority, removal of essential safety without an equivalent control, slower stigmatizing paths labeled equivalent, and assistive purchases without integration or maintenance are not complete accommodation design.

A mandatory training course that teaches users to tolerate an unnecessarily confusing interface is not accommodation. A manager informally allowing flexibility while workload and evaluation remain unchanged is not durable accommodation. A separate entrance that is technically usable but locked, remote, or dependent on finding staff is not meaningful equivalence. A medical screening program that sorts people out without redesigning demand is selection, not fit design.

Nor is every preference an accommodation claim that overrides all constraints. The archetype requires a credible capacity-demand relation, an essential outcome, evaluation of affected parties, and proportionate option selection. It can conclude that a requested method is unsafe or infeasible, but only after examining alternative controls and documenting the residual mismatch and review path.

prose_tradeoffs:
Common defaults conflict with personalization, privacy with support information, flexibility with safety consistency, and independence with human assistance. These require governed options rather than one universal mode.

Inclusive defaults reduce request burden and maintenance diversity, yet a default optimized for broad coverage can still exclude people at the tails. Personalization increases fit but introduces configuration, support, testing, and continuity costs. The appropriate portfolio usually combines a well-tested baseline, user-adjustable parameters, a small set of maintained alternative paths, and a governed route for individual exceptions.

More information may help tailor support, but disclosure can create stigma, discrimination, security exposure, and future misuse. Collect functional constraints and preferences at the narrowest useful scope, separate them from unrelated records, and make access visible. If staff need operational instructions, give them the instruction—not necessarily the diagnosis behind it.

Human assistance can restore access quickly and handle ambiguity that automation cannot. It can also reduce privacy, introduce scheduling delay, create dependency, or shift uncompensated work to families and colleagues. Treat assistance as an explicit, resourced mechanism with consent, coverage, training, backup, and boundaries rather than an invisible patch.

Accommodation can impose costs on other users or workers: a schedule change may affect team coordination, an alert may increase sensory load for someone else, or a manual fallback may create staff workload. These conflicts require transparent multi-party design and proportionate burden allocation. They do not justify defaulting to the status quo, whose burdens are often merely less visible.

prose_invariants_to_preserve:
- Essential outcome, inherited method, and safety constraint remain distinct.
- Capacity evidence represents range and fluctuation, including excluded nonusers.
- Affected people have choice and can contest mismatch assumptions.
- Accommodation does not silently shift unreasonable burden.
- Privacy, dignity, autonomy, and nonretaliation are preserved.
- Alternate modes receive equivalent testing, maintenance, and change control.
- Real-context validation and rapid repair remain available.

Preserving dignity does not mean avoiding measurement; it means measuring the system without making the person prove worthiness. Participation must be voluntary and informed, with safe ways to disagree, decline a proposed aid, or request another path. Data about functional need should be separated from general performance management and access-controlled according to purpose.

Preserving equity means evaluating both access to accommodation and quality after accommodation. A technically available path that takes longer, costs more, provides less privacy, limits advancement, or depends on discretionary goodwill is structurally unequal. Compare who knows the path exists, who can request it, who waits, who is denied, who carries coordination work, and who receives timely repair.

Safety is an invariant and a subject of redesign, not a blanket veto. When an inherited method is defended as safe, identify the hazard and required control. Alternative controls may provide equivalent or better risk reduction. When equivalence cannot be established, record the residual risk, decision authority, affected person's role, and review trigger.

prose_target_outcomes:
Expect greater safe access and completion, lower avoidable effort and error, less stigma and forced disclosure, more resilient performance, reduced workaround debt, and better-maintained inclusive systems.

Track outcome quality and sustainability, not request volume alone. Useful measures include completion among previously excluded users, error and injury rates, avoidable effort, time and delay, abandonment, support dependency, disclosure events, repair time, recurrence, parity after system changes, and distribution across groups and modalities. Pair quantitative signals with accounts of agency, dignity, predictability, and perceived safety.

A strong intervention can benefit people beyond the initially named group: adjustable timing supports fatigue and interruptions; clear state and recovery support novice and expert users; reduced force and neutral posture reduce cumulative injury; multiple communication modes improve resilience during channel failure. These spillovers are evidence of robust design, but they should not erase the need for individual accommodation where universal defaults cannot resolve conflicting or uncommon needs.

prose_parameter_dimensions:
Tune user and context segmentation, capability dimensions, evidence granularity, essential-function threshold, customization range, safety margin, disclosure minimum, equivalent-outcome standard, support level, repair time, backup capacity, and review cadence.

### Workload, rate, and complexity

Tune intensity, duration, repetition, concurrency, precision, and recovery separately. Lowering the average workload may leave a peak bottleneck untouched. Slowing a task can reduce time pressure but increase exposure and fatigue; splitting a task can lower memory load but add transitions. Complexity can be reduced, scaffolded, externalized, or distributed without removing the meaningful challenge or decision.

### Modality and interaction freedom

Tune which sensory and motor channels are required, whether modalities can be combined, and whether users can switch without losing state. Avoid making the alternative path a dead end: equivalent error messages, authentication, help, confirmation, appeal, and recovery matter as much as the happy path. Assistive compatibility should be tested as an interaction among tool, content, device, policy, and support—not assumed from nominal standards conformance.

### Personalization breadth and control

Set the range of adjustable values, who may change them, whether changes persist, and how defaults are chosen. Too little range creates false flexibility; too much unbounded variation can create safety, support, and maintenance problems. Favor user-controlled reversible settings inside a validated envelope, with accountable exception handling beyond it.

### Evidence, privacy, and review cadence

Tune evidence to the least intrusive information needed for the decision. Prefer functional need and desired outcome over diagnosis. Set expiration and revalidation only when the underlying context is likely to change and the benefit justifies the burden. Review cadence should follow risk, system change, failure history, and capacity fluctuation—not an arbitrary annual demand for recertification.

### Service and recovery levels

Define provisioning lead time, support availability, maximum outage, fallback capacity, escalation time, repair time, and communication during failure. A low-frequency accommodation can still be mission-critical. Service targets should reflect consequence of loss rather than average usage volume.

prose_variants_and_near_names:
Digital-interface accommodation emphasizes modality, navigation, timing, cognition, assistive technology, and recovery. Physical and ergonomic accommodation emphasizes reach, force, posture, mobility, fatigue, and environment. Organizational accommodation emphasizes pace, interruption, schedule, location, communication, and support.

The digital variant must preserve complete interaction loops: discovery, authentication, entry, validation, error correction, help, confirmation, appeal, and recovery. The physical variant must evaluate sustained and cumulative exposure rather than one-time capability. The organizational variant must make flexibility operational through workload, staffing, decision rights, communication norms, and manager capacity rather than relying on discretionary goodwill.

Near names should route into this parent only when they retain the capacity-demand, equivalent-outcome, dignity, implementation, and monitoring logic. “Accessibility design” may refer broadly to preventive inclusive design; “ergonomic accommodation” may refer narrowly to physical fit; and “design for human capacity” is an exact intent-level alias. A tool name, compliance checklist, medical evaluation, or request form remains a mechanism, not a separate archetype.

short_definition: Adapts digital modality, navigation, input, timing, state visibility, cognitive demand, assistive compatibility, and recovery so the complete interaction remains usable.

distinctive_feature: Interaction state, multimodality, device and assistive-tool interoperability, timing, error recovery, and release-parity risks dominate the mismatch.

distinct_from_parent: The parent spans physical, organizational, communication, environmental, and service demands; this variant concentrates the lifecycle on end-to-end digital interaction.

still_same_parent_because: It uses the same essential-outcome frame, capacity-demand diagnosis, option set, equivalence gate, operational ownership, failure recovery, and longitudinal revision.

use_when: A required digital workflow depends on one sensory or motor modality.

use_when: Timeouts, rapid state change, memory load, or inaccessible recovery create exclusion.

use_when: Nominal standards conformance does not establish real assistive-technology compatibility.

use_when: The accessible path repeatedly lags behind changes to the primary interface.

example: Preserves keyboard, screen-reader, zoom, adjustable-time, error-correction, status, appeal, and recovery parity through the full benefit-application workflow.

example: Maintains voice and alternative-input operation after interface releases and provides an accessible fallback when a configured integration fails.

failure_modes: Keyboard access reaches the happy path but not error recovery or appeal.

failure_modes: Timeout controls ignore reading, motor, fatigue, or interruption variability.

failure_modes: Automated checks substitute for representative in-context use.

failure_modes: Security or release changes break assistive compatibility without rapid repair.

name: Physical and Ergonomic Task Accommodation

short_definition: Adapts force, reach, posture, repetition, mobility, environmental exposure, fatigue, tools, sequencing, and recovery while preserving the essential outcome and safety.

distinctive_feature: Biomechanical demand, cumulative exposure, physical environment, tool fit, mobility, energy expenditure, and recovery opportunity dominate.

distinct_from_parent: This variant prioritizes physical and environmental measurement and may require engineering, facilities, occupational-safety, or equipment mechanisms.

still_same_parent_because: It retains the parent's method-neutral outcome, representative capacity envelope, demand transformation, dignity gate, maintained implementation, and revision loop.

use_when: A task is possible once but unsafe or unsustainable across its real duration and frequency.

use_when: Fixed workstation, route, posture, tool, or environmental assumptions exclude capable people.

use_when: Pain, fatigue, strength, dexterity, mobility, or sensory exposure varies across people or time.

use_when: Informal coworker assistance conceals a recurring physical-demand mismatch.

example_domains: workplaces products transportation facilities healthcare

domain: manufacturing

example: Preserves inspection quality through adjustable fixtures, tool balance, task rotation, seated options, bounded pacing, and backup during equipment failure.

domain: transportation

example: Treats booking, boarding, securement, disruption, assistance, and recovery as one maintained equivalent trip path rather than only providing a nominal vehicle feature.

failure_modes: One-time capability testing misses cumulative load and recovery debt.

failure_modes: An inherited strength or posture requirement is asserted as essential without evidence.

failure_modes: Equipment is delivered without configuration, training, spares, or integration.

failure_modes: Recurring lifting, routing, or troubleshooting shifts invisibly to coworkers.

name: Organizational Schedule and Cognitive Accommodation

short_definition: Adapts rate, interruption, memory, communication, location, schedule, social exposure, stress, recovery, and support to fluctuating capacity within an organization.

distinctive_feature: Temporal, attentional, communication, social, workload, staffing, and discretionary decision demands dominate; the accommodation must be embedded in operating governance.

distinct_from_parent: This variant focuses less on a single artifact and more on work allocation, cadence, norms, evaluation, manager capacity, handoff, and protected recovery.

still_same_parent_because: It uses the same capacity-demand map, equivalent-outcome test, consent and dignity safeguards, accountable delivery, burden measurement, and adaptive feedback.

use_when: Fixed schedules, rapid switching, ambiguous communication, or interruption drive avoidable failure.

use_when: A flexible policy exists but workload, staffing, evaluation, or manager authority makes it unusable.

use_when: People must repeatedly disclose needs to different decision makers or renegotiate stable support.

use_when: Informal colleagues absorb memory, coordination, communication, or coverage work.

domain: workplace

example: Aligns flexible hours with workload, meeting norms, response expectations, manager authority, team coverage, evaluation criteria, and a private revision route.

domain: education

example: Preserves the learning construct through alternative response modes, bounded pacing, accessible materials, low-distraction conditions, feedback, and recovery.

failure_modes: Flexibility is nominal because workload and response-rate expectations do not change.

failure_modes: A manager's goodwill replaces stable, contestable operating rights.

failure_modes: Repeated disclosure produces stigma and administrative exhaustion.

failure_modes: Reduced assignment without opportunity or evaluation parity becomes paternalistic restriction.

near_names:
Accessibility Design
Ergonomic Accommodation
Design for Human Capacity
Adaptive User-Fit Design


Hypothesis Test Power Calibration

type: archetype

source: generated

batch: Second Iteration/Batch 08

aliases:
test sensitivity calibration
hypothesis test power planning
minimum detectable effect planning
study power calibration
operating characteristic planning
power analysis and sample size planning

slug: uncertainty

slug: threshold

slug: variability

slug: calibration

components:
Decision-Relevant Effect Threshold
Null, Alternative, and Estimand Frame
Error-Rate Budget
Noise and Variance Profile
Sample Size and Allocation Plan
Operating Characteristic Model
Feasibility and Ethics Constraint
Attrition and Missingness Allowance
Sensitivity Scenario Grid
Pre-Registered Interpretation Rule
Variance-Reduction Design Move

mechanisms:
Closed-Form Power Calculation
Simulation-Based Power Analysis
Minimum Detectable Effect Table
Operating Characteristic Curve
Power Sensitivity Grid
Pre-Analysis Power Statement
Pilot Variance Estimation

essence_one_line: Design a hypothesis test around the effect that would actually matter, then tune sample size, noise control, allocation, and error rates so the test has adequate power to detect it.

compression_statement: Hypothesis Test Power Calibration converts statistical inference from a passively observed p-value exercise into a design-time sensitivity contract: for a specified meaningful effect size, model, variability profile, missingness or attrition expectation, alpha level, and resource budget, the test must reach a target probability of rejecting the null when that effect is truly present.

core_intuition: A test that cannot detect the effect its users care about is not merely inconclusive; it was under-instrumented. Power calibration forces the design to state what would count as worth detecting and whether the planned data can realistically reveal it.

canonical_formula: Choose design D so Power(D, delta*, alpha, model, noise) = P(reject H0 | true effect = delta*) >= 1 - beta, while respecting cost, ethics, time, and feasibility constraints.

structural_problem: An empirical decision depends on a hypothesis test, but the planned data collection, measurement precision, or thresholding scheme may be too weak to distinguish a meaningful true effect from noise.

trigger_conditions: A study, experiment, monitoring plan, or test is being designed before data collection or before a major decision gate.

trigger_conditions: Decision-makers can state a minimum effect size, exceedance, improvement, degradation, or difference that would matter in practice.

trigger_conditions: False negatives are consequential enough that low sensitivity would make a nominally rigorous test misleading.

trigger_conditions: Sample size, measurement frequency, allocation ratio, follow-up duration, or signal-to-noise control can still be changed.

trigger_conditions: A null result would otherwise be treated as evidence of no meaningful effect, even when the design may simply be underpowered.

symptoms: The analysis plan specifies alpha but not beta, target power, or a minimum detectable effect.

symptoms: The sample size is justified by convenience, budget, or precedent rather than by detection requirements.

symptoms: A non-significant result is likely to be interpreted as absence of effect without checking detectable magnitude.

symptoms: The test is sensitive only to large effects, while the decision requires detecting smaller but policy-relevant changes.

symptoms: Measurement noise, subgrouping, attrition, multiple testing, or interim stopping rules reduce effective power after the fact.

root_tension: The design must balance epistemic sensitivity against cost, ethics, time, operational burden, and error-rate discipline; maximizing detection probability without boundaries can waste resources or inflate false positives, while minimizing resources can produce false reassurance.

anti_signatures: The task is purely descriptive and no inferential decision or claim threshold is planned.

anti_signatures: The true target effect cannot be bounded or translated into an interpretable detection requirement.

anti_signatures: The main problem is biased measurement, confounding, or invalid randomization rather than inadequate sensitivity.

anti_signatures: The design is already fixed and the immediate need is transparent limitation reporting rather than redesign.

anti_signatures: The decision rule is deterministic, not statistical, and does not depend on sampling uncertainty.

structural_intervention: Before treating a hypothesis test as decision-ready, specify the meaningful effect to detect, choose acceptable Type I and Type II error risks, model the relevant noise and design assumptions, then adjust the design until the test reaches the target operating characteristics or explicitly records that it cannot.

action_logic: Define the decision-relevant contrast: null, alternative, estimand, population, measurement window, and minimum meaningful effect.

action_logic: Set a target error budget that includes alpha, desired power, tolerance for false negatives, and any multiple-testing or sequential-testing corrections.

action_logic: Model the planned design, including sample size, allocation, variance, clustering, missingness, attrition, measurement reliability, and feasible follow-up duration.

action_logic: Compute or simulate power across plausible effect sizes and nuisance-parameter assumptions, then identify the minimum detectable effect under the proposed design.

action_logic: Revise sample size, measurement precision, allocation, blocking, stratification, endpoint choice, or analysis model until sensitivity is adequate or the limitation is made explicit.

action_logic: Prespecify the power rationale so later interpretation of significant and non-significant findings is bounded by the design sensitivity actually achieved.

decision_rules: Use this archetype when a null or weak finding would affect action and under-detection would be costly or misleading.

decision_rules: Calibrate to a minimum effect that matters substantively, not merely to the smallest effect a large sample can make significant.

decision_rules: Treat a convenience sample as inadequate unless it demonstrably reaches the target power for the chosen effect and model assumptions.

decision_rules: If assumptions about variance, attrition, baseline rate, or clustering are uncertain, examine a sensitivity range rather than a single optimistic calculation.

decision_rules: If the target power cannot be reached under ethical or resource constraints, change the decision question or report the design as exploratory rather than confirmatory.

target_invariants: The target effect size remains tied to practical, clinical, operational, or policy significance.

target_invariants: The false-positive and false-negative risks are both visible, not silently reduced to alpha alone.

target_invariants: Power assumptions are documented before outcome interpretation and are not retrofitted to justify a result.

target_invariants: The design sensitivity accounts for measurement noise, missingness, clustering, multiplicity, and attrition when those conditions are relevant.

target_invariants: A non-significant result is interpreted only relative to the effect magnitudes the design could reasonably detect.

expected_outcomes: Studies avoid being launched with sample sizes or measurement plans that cannot answer the intended question.

expected_outcomes: Null, weak, or inconclusive results are interpreted with clearer sensitivity bounds.

expected_outcomes: Resources are allocated toward the measurements, participants, follow-up windows, or variance-reduction moves that most improve inferential value.

expected_outcomes: Decision-makers can distinguish no detected effect from no meaningful effect with less ambiguity.

expected_outcomes: The evidential standard becomes more reproducible because design assumptions and operating characteristics are explicit.

prose_essence:
Hypothesis Test Power Calibration is the design pattern that asks whether a planned empirical test can actually detect the effect its users care about. It moves statistical inference upstream from “run the test and inspect the p-value” to “state the meaningful effect, model the uncertainty, and make sure the design has enough sensitivity to resolve the decision.”

The pattern is most useful when a non-significant result would be treated as evidence that nothing important happened. Without power calibration, that interpretation can be false. The study may simply have lacked enough observations, precision, follow-up time, allocation balance, or variance control to detect the effect.

prose_tradeoffs:
Power calibration exposes tradeoffs rather than eliminating them. Larger samples and longer follow-up can improve sensitivity, but they may be costly, slow, intrusive, or ethically questionable. Lower alpha can reduce false positives, but it may require more data to preserve power. Variance reduction can improve detection, but it may narrow generalizability if it removes real-world heterogeneity.

There is also a risk of overpowering. A massive dataset can make trivial differences significant. The answer is not simply “maximize power.” The answer is to align power with effect sizes that matter.

Analytical Power Calculation Variant

short_definition: Use closed-form statistical formulas to choose sample size or estimate power for a standard hypothesis test.

distinctive_feature: Emphasizes formula-based planning rather than simulation or empirical pilot calibration.

distinct_from_parent: The parent archetype includes any design-time sensitivity calibration; this variant is the formula-driven implementation path.

still_same_parent_because: It shares the same goal of matching design sensitivity to a meaningful effect under explicit error risks.

use_when: The planned test has a well-established analytical power formula.

use_when: Distributional, independence, and variance assumptions are sufficiently stable for planning purposes.

example_domains:
A/B testing
two-arm clinical trials
survey sample-size planning

name: Simulation-Based Power Calibration Variant

short_definition: Estimate power by repeatedly simulating data under plausible true effects and applying the planned analysis procedure.

distinctive_feature: Tests the whole planned analysis pipeline under assumed data-generating scenarios.

distinct_from_parent: The parent includes all power calibration; this variant specifically relies on scenario simulation.

still_same_parent_because: It still answers the parent question: whether the planned design can detect the effect that matters.

use_when: The design includes clustering, missingness, adaptive rules, nonstandard estimators, or complex endpoints.

use_when: Closed-form calculations hide important practical assumptions or are unavailable.

example_domains:
cluster-randomized trials
environmental sensor networks
longitudinal studies

name: Minimum Detectable Effect Planning Variant

short_definition: Present the effect size that the proposed design can detect, then decide whether that sensitivity is substantively adequate.

distinctive_feature: Reverses the question from how much N is needed to what effect magnitude the available design can resolve.

distinct_from_parent: The parent includes both required-N and achieved-sensitivity reasoning; this variant foregrounds the achieved sensitivity side.

still_same_parent_because: It is still a power-calibration move because it ties interpretation to detection probability for meaningful effect sizes.

use_when: The sample size or observation budget is mostly fixed.

use_when: Stakeholders need to know what magnitude of effect a null result could actually rule out.

example_domains:
policy pilots
legacy dataset analysis
fixed-budget monitoring programs

name: Attrition-Adjusted Power Calibration Variant

short_definition: Calibrate design sensitivity after expected dropout, nonresponse, missing observations, or unusable measurements are accounted for.

distinctive_feature: Treats retained usable information, not recruited or nominal sample size, as the power-relevant quantity.

distinct_from_parent: The parent handles general power calibration; this variant foregrounds attrition as the dominant sensitivity threat.

still_same_parent_because: It preserves the same structure of matching design sensitivity to a target effect under explicit assumptions.

use_when: The study involves follow-up over time or participation is likely to decline.

use_when: Missingness, noncompliance, or unusable data would materially reduce effective sample size.

example_domains:
clinical follow-up trials
longitudinal surveys
remote sensor deployments

near_names: Sample Size Calculation and Justification


Hypothesis Testing Frame

batch: First Iteration/Batch 30

slug: probability

components:
Claim Under Test
Default Claim
Alternative Claim
Test Evidence
Evidence Threshold
Error-Cost Profile
Decision Rule
Interpretation Rule
Interpretation Limit
Effect Context
Power or Sensitivity Check
Multiplicity Context
Precommitment Record
Follow-Up Trigger

mechanisms:
Null Hypothesis Significance Test
Decision Threshold Rule
Quality Acceptance Test
A/B Test Interpretation Protocol
Legal Burden-of-Proof Analog
Inspection Pass/Fail Test
Falsification Protocol
Equivalence or Noninferiority Test
Sequential Review Gate
Scientific Claim Evaluation Template

essence_one_line: Frame a claim against a default alternative so evidence can change belief or action under explicit error risks.

compression_statement: When a claim must be evaluated, define the default, alternative, evidence threshold, and error costs before interpreting results.

core_intuition: Evidence does not interpret itself. A result becomes meaningful only relative to a claim, a default presumption, a competing alternative, a threshold for changing status, and the consequences of being wrong in each direction.

canonical_formula: claim_under_test + default_claim + alternative_claim + test_evidence + evidence_threshold + error_cost_profile -> decision_rule + interpretation_limit

structural_problem: A claim is assessed informally or after-the-fact, allowing ambiguous evidence thresholds, moving standards, one-sided interpretation, and confusion between evidence, action, causality, and practical importance.

trigger_conditions: A decision depends on whether a claim should be accepted, rejected, escalated, or left unresolved.

trigger_conditions: Evidence is noisy, partial, probabilistic, contested, or open to multiple interpretations.

trigger_conditions: Actors can move thresholds after observing evidence or emphasize whichever result supports their preferred conclusion.

trigger_conditions: False positives and false negatives have materially different costs.

trigger_conditions: A default presumption or incumbent state should not change without a clear evidentiary burden.

trigger_conditions: A result must be communicated with limits rather than converted into simplistic proof or disproof.

symptoms: People argue over whether evidence is “enough” only after the result is known.

symptoms: A non-significant or inconclusive result is treated as proof that no effect exists.

symptoms: A significant or threshold-crossing result is treated as proof of importance, causality, or safety without checking design limits.

symptoms: Teams change from a planned metric to a convenient metric after seeing the data.

symptoms: The default position shifts depending on who benefits from the interpretation.

symptoms: False alarm and missed-detection risks are discussed only after a decision has already been made.

root_tension: The system needs to learn from evidence, but evidence can be interpreted opportunistically unless the claim, default, threshold, and error risks are fixed before interpretation.

anti_signatures: The task is open-ended exploration without a claim being evaluated or acted upon.

anti_signatures: The evidence source is already known to be biased or confounded; sampling or causal controls must come first.

anti_signatures: The main problem is many simultaneous claims; Multiple-Testing Discipline is primary.

anti_signatures: The main problem is practical magnitude rather than claim status; effect-size or decision-relevance framing is primary.

anti_signatures: The decision requires a full belief distribution and continuous updating; Bayesian Belief Updating may be primary.

structural_intervention: Define the claim under test, the default claim, the alternative claim, the evidence source, the evidence threshold, the false-positive and false-negative costs, the decision rule, and the interpretation limits before treating evidence as support for action.

action_logic: State the claim in a form that can be exposed to evidence rather than protected by vague language.

action_logic: Choose the default claim or presumption that remains in place unless evidence is sufficient to displace it.

action_logic: Define the alternative claim and distinguish superiority, difference, equivalence, noninferiority, safety, quality, or burden-of-proof forms.

action_logic: Specify which evidence will be used and what assumptions or measurement limits govern it.

action_logic: Set the evidence threshold in light of the cost of false positives and false negatives.

action_logic: Apply a decision rule that separates “support for the alternative” from “insufficient evidence,” “requires follow-up,” and “safe to act.”

action_logic: Record interpretation limits so threshold crossing does not become overclaiming and threshold failure does not become false proof of absence.

decision_rules: Set the threshold before observing the evidence whenever the result could influence the threshold.

decision_rules: Make the threshold stricter when false positives are costly and more sensitive when false negatives are costly, while documenting the tradeoff.

decision_rules: Do not treat failure to reject a default as proof the default is true unless the frame is designed for equivalence or noninferiority and has adequate sensitivity.

decision_rules: Do not treat threshold crossing as evidence of causal effect unless causal design and confounder controls support that interpretation.

decision_rules: Require companion disciplines when the test is underpowered, one among many attempted tests, selected from biased evidence, or practically trivial.

target_invariants: The default and alternative claims remain stable during interpretation.

target_invariants: The evidence threshold is not moved after seeing the result.

target_invariants: Error costs are explicit rather than hidden inside ritual thresholds.

target_invariants: Inconclusive evidence remains distinguishable from proof of no effect.

target_invariants: Evidence status remains distinguishable from practical importance, causal attribution, generalization, and ethical acceptability.

expected_outcomes: Lower risk of post-hoc threshold shopping.

expected_outcomes: Better calibration between evidence and action.

expected_outcomes: Clearer communication of what a result does and does not show.

expected_outcomes: Improved resistance to confirmation bias, overclaiming, and arbitrary burden-shifting.

prose_essence:
Hypothesis Testing Frame is the pattern of turning an ambiguous evidentiary question into a disciplined claim-evaluation structure. It asks: what is the claim, what remains true by default, what alternative would displace that default, what evidence is allowed to bear on the choice, how strong must that evidence be, and what mistakes are we trying to avoid?

The archetype is not a p-value, a statistical test, or a proof ritual. It is the surrounding architecture that makes a result interpretable. A statistical test may implement the archetype, but so can a quality acceptance standard, a legal burden of proof, a safety approval gate, or a structured diagnostic review.

prose_when_to_use:
Use this archetype when a claim must be accepted, rejected, escalated, or left unresolved under uncertainty. It is especially useful when a result could be interpreted opportunistically, when actors might move standards after seeing evidence, or when the two major mistakes—false acceptance and false rejection—carry different costs.

It is less useful for open exploration with no claim yet, for evidence that is visibly biased or confounded before testing begins, or for situations where continuous belief updating is the primary task. In those cases, pair it with other archetypes rather than forcing all evidence work into a single threshold frame.

prose_structural_problem:
The structural problem is not merely lack of data. It is the absence of a stable interpretation frame. A team sees evidence, then decides what it was testing. A reviewer accepts a claim because the result looks impressive, without saying what default was displaced. A non-detection is treated as proof of no effect even though the evidence may have been too weak to detect anything. A threshold is chosen because it supports a preferred conclusion.

Without the frame, claim evaluation becomes vulnerable to ambiguity, confirmation bias, burden shifting, threshold shopping, and overclaiming. The same evidence can be used to support incompatible conclusions because the claim, default, threshold, and error costs were never fixed.

prose_intervention_logic:
The intervention begins by naming the claim under test. It then defines the default claim that remains unless evidence is sufficient, and the alternative claim that would justify changing belief or action. Next it specifies admissible evidence, the evidence threshold, and the error-cost profile: what happens if we accept a false claim, and what happens if we miss a true one?

Only after those pieces are explicit should the evidence be interpreted. The decision rule converts evidence into a status: reject the default, fail to reject it, escalate, continue monitoring, or require follow-up. The interpretation rule then states what the result does not establish. A threshold crossing does not automatically prove causality, practical importance, generalizability, or safety. A failure to cross a threshold does not automatically prove absence.

prose_key_components:
- **Claim Under Test (`claim_under_test`)**: This is the assertion being exposed to evidence. It prevents the conversation from drifting between several claims, such as “the intervention works,” “the intervention is large enough to matter,” and “the intervention caused the change.”
- **Default Claim (`default_claim`)**: This is the baseline presumption that stays in place unless evidence is strong enough to displace it. It might be no improvement, current policy retained, no defect found, no safety approval, or innocence until proof.
- **Alternative Claim (`alternative_claim`)**: This is the proposed departure from the default. It can assert improvement, difference, equivalence, noninferiority, risk, defect, or causal change.
- **Test Evidence (`test_evidence`)**: This names the observations or measurements allowed to bear on the claim. It ties the test to actual evidence instead of letting any convenient signal count.
- **Evidence Threshold (`evidence_threshold`)**: This sets how much evidence is enough to change interpretation or action. It can be numerical, procedural, legal, operational, or qualitative, but it must not move after seeing the result.
- **Error-Cost Profile (`error_cost_profile`)**: This explains the consequences of false positives and false negatives. It is what keeps the threshold from being arbitrary or purely conventional.
- **Decision Rule (`decision_rule`)**: This translates evidence into action status. It distinguishes “support the alternative,” “retain the default,” “insufficient evidence,” “monitor,” and “requires follow-up.”
- **Interpretation Rule (`interpretation_rule`)**: This constrains what can be said after the decision rule fires. It prevents significance from becoming importance, non-significance from becoming proof of absence, and association from becoming causation.
- **Interpretation Limit (`interpretation_limit`)**: This marks the boundary of the test frame. It reminds users when sampling, confounding, multiplicity, power, or magnitude questions remain unresolved.

Optional supporting components include effect context, power or sensitivity checks, multiplicity context, precommitment records, and follow-up triggers. These often activate neighboring archetypes.

prose_common_mechanisms:
Mechanisms implement the archetype; they are not the archetype itself.

- **Null Hypothesis Significance Test (`null_hypothesis_significance_test`)**: A formal statistical mechanism for evaluating a default/null claim against an alternative under assumptions and thresholds. It implements the archetype only when the claim frame and interpretation limits are explicit.
- **Decision Threshold Rule (`decision_threshold_rule`)**: A procedural mechanism that turns evidence into a status change. Thresholds are useful, but a threshold alone is not the archetype.
- **Quality Acceptance Test (`quality_acceptance_test`)**: An operational mechanism for accepting, rejecting, or reworking a batch, product, service, or process. It works when acceptance criteria and error costs are explicit.
- **A/B Test Interpretation Protocol (`ab_test_interpretation_protocol`)**: A product or service experimentation mechanism that interprets measured differences against planned metrics and launch criteria.
- **Legal Burden-of-Proof Analog (`legal_burden_of_proof_analog`)**: A governance mechanism that assigns a default presumption and evidence burden before changing a status or imposing a consequence.
- **Inspection Pass/Fail Test (`inspection_pass_fail_test`)**: A classification mechanism for checking whether an item meets criteria. It becomes weak when the criteria are arbitrary or adjusted after inspection.
- **Falsification Protocol (`falsification_protocol`)**: A mechanism for stating what would count against a favored claim before evidence is reviewed.
- **Equivalence or Noninferiority Test (`equivalence_or_noninferiority_test`)**: A mechanism for showing that a difference is small enough or that a new option is not unacceptably worse.
- **Sequential Review Gate (`sequential_review_gate`)**: A workflow mechanism for interpreting evidence at predefined milestones. Repeated looks may require Multiple-Testing Discipline.
- **Scientific Claim Evaluation Template (`scientific_claim_evaluation_template`)**: A template mechanism that prompts users to document claim, default, alternative, evidence, threshold, assumptions, error costs, and limits.

prose_failure_modes:
A common failure is **post-hoc thresholding**, where the evidence threshold is selected after seeing the result. Another is **default claim drift**, where the default changes to favor the preferred conclusion. **p-value ritualism** occurs when a statistical label replaces reasoning about claim, assumptions, error costs, and limits.

**Non-significance as proof of absence** is especially dangerous: failing to cross a threshold may mean evidence was weak, noisy, underpowered, or poorly aimed. **Practical-irrelevance blindness** occurs when a threshold-crossing result is treated as important despite tiny magnitude. **Multiplicity leakage** occurs when a single claim frame is applied to a result selected from many unreported attempts. **Causal overreach** occurs when the test frame is treated as proof of cause without causal design support.

prose_neighbor_distinctions:
**Multiple-Testing Discipline** governs many attempted claims, comparisons, metrics, or subgroups. Hypothesis Testing Frame can structure one planned claim; multiplicity discipline becomes necessary when the claim was selected from a larger search space.

**Power-Aware Design** asks whether evidence collection can detect an effect worth acting on. Hypothesis Testing Frame asks how to interpret evidence against a default and alternative. A weak test frame can produce “no evidence” without being sensitive enough to mean “no effect.”

**Effect Size Reporting** asks how large or practically important the effect is. Hypothesis Testing Frame asks whether evidence crosses a claim threshold. A result can be statistically supported and practically trivial.

**Uncertainty Explicitness** makes uncertainty visible. Hypothesis Testing Frame turns evidence and uncertainty into claim-status decisions under explicit error costs.

**Confounder Control** protects causal interpretation from third-variable distortion. Hypothesis Testing Frame protects claim interpretation from ambiguous thresholds and burden shifting.

**Bayesian Belief Updating** revises beliefs continuously using priors and evidence. Hypothesis Testing Frame usually creates a discrete claim-evaluation status, though Bayesian evidence can feed the frame.

prose_cross_domain_examples: In scientific research, the archetype structures whether evidence supports a planned claim rather than allowing after-the-fact result storytelling. In product analytics, it keeps a team from shipping based on whichever metric looks best after the experiment. In manufacturing, it makes acceptance and rejection criteria auditable. In safety governance, it defines the burden of evidence before approving high-risk action. In legal or compliance contexts, it preserves the presumption and standard of proof. In diagnosis, it clarifies what evidence would support or weaken a working explanation.

prose_non_examples: A p-value alone is not this archetype. A pass/fail rule with no explicit claim, default, threshold rationale, or error-cost logic is only a mechanism. A dashboard where many comparisons are searched and one favorable result is reported needs Multiple-Testing Discipline first. A causal claim drawn from confounded data needs Confounder Control. A report saying “no difference found” from a weak or tiny study needs Power-Aware Design before the non-detection can be interpreted confidently.

prose_tradeoffs:
The archetype trades interpretive flexibility for discipline. Pre-specified thresholds prevent opportunistic interpretation, but they can also be too rigid when unexpected evidence appears. Stricter thresholds reduce false positives but raise false-negative risk. More sensitive thresholds reduce misses but increase false alarms. Simple decision labels communicate clearly but can hide nuance.

The frame can also institutionalize power. Whoever defines the default and threshold can shape which claims are easy or hard to establish. This is useful when the default protects people from harm or arbitrary action, but dangerous when it protects incumbents from legitimate challenge.

prose_invariants_to_preserve:
The default claim, alternative claim, and evidence threshold must remain stable during interpretation. The frame must preserve the difference between insufficient evidence and evidence of absence. It must also preserve the difference between evidential status, causal attribution, practical magnitude, generalization, and ethical acceptability.

The archetype should never allow a mechanism to replace the frame. A p-value, pass/fail label, inspection score, burden-of-proof phrase, or significance star is only meaningful when embedded in the surrounding claim-evaluation architecture.

prose_target_outcomes:
The target outcomes are disciplined claim evaluation, reduced threshold shopping, clearer burden of evidence, better calibration between evidence and action, and more honest communication of results. The frame should make it easier to say not only “what did we find?” but also “what were we trying to decide, what would have counted as enough, what mistakes were we guarding against, and what remains unsettled?”

A successful implementation does not eliminate uncertainty. It makes uncertainty actionable without pretending that the evidence proves more than it does.

prose_parameter_dimensions:
Important tuning dimensions include the stringency of the threshold, the asymmetry between false-positive and false-negative costs, the specificity of the alternative claim, the credibility and independence of evidence, the strength of assumptions, the availability of follow-up evidence, the decision stakes, and whether action is reversible.

A safety-critical frame usually sets a higher burden before approving action. A screening frame may lower the threshold to avoid missing plausible signals, but then require follow-up before irreversible action. A legal or governance frame may emphasize who bears the burden of proof. A product experimentation frame may balance false rollout risk against the cost of delaying a beneficial change.

prose_variants_and_near_names:
Recognized variants include the **Null–Alternative Statistical Test Variant**, **Superiority Test Variant**, **Equivalence / Noninferiority Variant**, **Burden-of-Proof Variant**, and **Falsification Frame Variant**. Near names include claim evaluation frame, null–alternative frame, evidence threshold frame, burden-of-proof frame, null hypothesis testing, and significance testing.

Collapsed candidates include p-value, statistical significance label, confidence interval, single pass/fail rule, and preregistration. These are useful mechanisms, statistics, representations, or support tools, but they should not be drafted as full archetypes here. **Error Tradeoff Calibration** remains a second-wave promotion candidate if it proves broader than the claim-testing frame.

name: Null–Alternative Statistical Test Variant

short_definition: A formal statistical variant that evaluates a null/default hypothesis against an alternative using a specified test statistic and threshold.

distinctive_feature: It uses statistical sampling logic and test statistics to instantiate the broader claim-evaluation frame.

distinct_from_parent: The parent also covers legal, operational, quality, scientific, and governance claim frames that are not formal statistical tests.

still_same_parent_because: It retains the same default, alternative, evidence threshold, error-cost, and interpretation-limit structure.

use_when: The evidence is quantitative and the statistical assumptions are adequate or explicitly checked.

use_when: A formal false-positive threshold is needed for claim interpretation.

use_when: The decision context can tolerate the abstraction from practical magnitude to evidential threshold, with effect magnitude handled separately.

Default Claim

role: Defines the null or baseline condition.

Alternative Claim

role: Defines the departure being tested.

Evidence Threshold

role: Sets the statistical criterion for interpretation.

Null Hypothesis Significance Test

role: Implements the formal statistical comparison.

Decision Threshold Rule

role: Turns the criterion into a decision status.

domain: Product experimentation

example: A team predefines activation-rate improvement as the alternative and interprets the result against a fixed threshold.

domain: Quality control

example: A production line tests whether failure rate exceeds an allowable baseline.

failure_modes: Treating a p-value threshold as proof rather than a decision rule under assumptions.

failure_modes: Ignoring effect magnitude, power, multiple testing, or confounding after crossing the threshold.

Superiority Test Variant

variant_type: subtype

short_definition: A variant where the alternative claims that one option, process, treatment, or policy performs better than the default or comparator.

distinctive_feature: The alternative is directional toward improvement or superiority rather than equivalence, noninferiority, or mere difference.

distinct_from_parent: The parent is neutral about what kind of alternative is being evaluated; this variant fixes the alternative as improvement.

still_same_parent_because: It still requires a default claim, alternative claim, evidence threshold, error-cost profile, and interpretation rule.

use_when: The decision depends on showing improvement beyond a default, incumbent, baseline, or competitor.

use_when: The cost of falsely claiming improvement is material.

use_when: Practical magnitude can be interpreted alongside evidential strength.

Alternative Claim

role: States the improvement hypothesis.

Effect Context

role: Clarifies whether the improvement is meaningful.

A/B Test Interpretation Protocol

role: Evaluates product or service superiority.

Quality Acceptance Test

role: Tests whether a new process exceeds a required standard.

example_domains: medicine product_design education operations

domain: Education

example: A school evaluates whether a new tutoring program improves reading gains over the existing program.

failure_modes: A tiny but detectable improvement is treated as decisive without effect-size or cost-benefit context.

Equivalence / Noninferiority Variant

short_definition: A variant where the aim is to show that a new option is close enough to, or not unacceptably worse than, an existing standard.

distinctive_feature: The threshold is a margin of acceptable difference rather than evidence of improvement.

distinct_from_parent: The parent names the generic claim frame; this variant reverses common intuitions about default and alternative by testing closeness or acceptable loss.

still_same_parent_because: It still frames a claim against a default with explicit evidence thresholds and error risks.

use_when: The new option may be cheaper, safer, simpler, fairer, faster, or more accessible if it is not meaningfully worse on a core outcome.

use_when: The decision needs a tolerance margin rather than a superiority claim.

use_when: False equivalence would be harmful if the new option actually underperforms beyond the acceptable margin.

Evidence Threshold

role: Defines the equivalence or noninferiority margin.

Interpretation Limit

role: Prevents “no detected difference” from being misread as equivalence.

Equivalence or Noninferiority Test

role: Implements the margin-based comparison.

domain: Software migration

example: A team tests whether a cheaper infrastructure setup is not meaningfully slower than the current one.

failure_modes: Treating failure to find a difference as evidence of equivalence without a pre-specified margin and adequate sensitivity.

Burden-of-Proof Variant

short_definition: A governance-oriented variant that assigns which side must provide evidence and what standard must be met before a presumption changes.

distinctive_feature: It emphasizes presumption, evidence burden, and procedural fairness rather than statistical estimation.

distinct_from_parent: The parent is broader; this variant centers institutional rules about who must prove what.

still_same_parent_because: The structural frame remains default claim, alternative claim, evidence threshold, error cost, and interpretation limit.

use_when: Changing the default can impose serious costs, penalties, liability, or rights restrictions.

use_when: A review body must protect against arbitrary or self-serving claim acceptance.

use_when: The evidence standard is social, legal, regulatory, or procedural rather than purely statistical.

Default Claim

role: Sets the protected presumption.

Error-Cost Profile

role: Explains why the burden is placed where it is.

Decision Rule

role: Defines the consequence of satisfying or failing the burden.

Legal Burden-of-Proof Analog

role: Implements presumption and burden in an institutional setting.

Scientific Claim Evaluation Template

role: Transfers burden logic into research or review.

domain: Safety review

example: A new high-risk process is presumed unsafe until it meets a defined evidence standard.

failure_modes: The burden is set to protect incumbents rather than to calibrate real error costs.

Falsification Frame Variant

short_definition: A variant that defines in advance what observation would count against a favored claim so evidence search does not become one-sided confirmation.

distinctive_feature: It emphasizes disconfirming evidence and claim vulnerability rather than threshold crossing alone.

distinct_from_parent: The parent can evaluate claims with many threshold structures; this variant asks what would make the claim fail.

still_same_parent_because: It uses the same default/alternative and interpretation architecture, but orients evidence collection toward potential refutation.

use_when: A team, researcher, leader, or community has a favored explanation that may attract confirmatory evidence.

use_when: The cost of being wrong is high enough to require active disconfirmation rather than passive confidence.

use_when: The claim is broad enough that ordinary positive examples do not meaningfully test it.

Interpretation Rule

role: Defines what would disconfirm or weaken the claim.

Test Evidence

role: Targets observations that could actually challenge the claim.

Falsification Protocol

role: Operationalizes active disconfirmation.

example_domains: science strategy diagnosis auditing incident_investigation

domain: Incident investigation

example: A root-cause team states what evidence would reject its leading explanation before reviewing logs.

failure_modes: The “falsification” test is too weak, allowing a favored claim to survive without serious exposure to counterevidence.

near_names:
Claim Evaluation Frame
Null–Alternative Frame
Evidence Threshold Frame
Burden-of-Proof Frame
Null Hypothesis Testing
Significance Testing