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Sensitivity Analysis Protocol

Vary key assumptions or parameters to see which ones materially change the conclusion.

The Diagnostic Story

Symptom: A recommendation looks precise and settled, but the assumptions feeding it are rough, contested, or undocumented. Stakeholders argue about the conclusion without examining which assumptions would actually change it. Small cosmetic variations leave the result unchanged, yet no plausible stress range has ever been tested. A single best-estimate answer is being presented as if it were more certain than the evidence warrants.

Pivot: Make uncertain assumptions explicit, vary them across defensible ranges or scenarios, measure how the conclusion responds, and surface the critical assumptions and flip points — the inputs where a plausible change would change the recommendation — so validation effort and monitoring can be concentrated there.

Resolution: Decision confidence becomes conditional and auditable: stakeholders know which assumptions the conclusion is fragile to, validation resources are directed at those inputs first, and the recommendation is accompanied by the stress range rather than presented as a single number. Fragile conclusions become visible before commitment rather than after.

Reach for this when you hear…

[financial modeling] “The IRR looks great at the base case, but what happens if volume is 20% lower — does the whole project flip negative?”

[climate policy] “The cost-benefit ratio changes by a factor of three depending on the discount rate, so we can't just present one number.”

[clinical decision support] “That threshold was set when the false-positive rate was estimated at 5% — what's the recommendation if it's actually 15%?”

Mechanisms / Implementations

  • One-way Sensitivity Analysis: Varies one parameter at a time.
  • Two-way or Multi-way Sensitivity Analysis: Multi-way analysis varies combinations of inputs.
  • Scenario Variation: Groups assumptions into coherent worlds.
  • Threshold Analysis: Searches for the point where the conclusion changes.
  • Tornado Chart: A tornado chart visualizes which inputs produce the largest outcome movement.
  • Sensitivity Table: A sensitivity table records parameters, ranges, outcome responses, materiality judgments, and critical assumptions.
  • Probabilistic Sensitivity Simulation: Samples uncertain inputs from distributions or weighted cases.
  • Assumption Stress-test Workshop: A workshop mechanism brings stakeholders or experts into range-setting and interpretation.

Abstractions this archetype builds on — directly (a source ingredient) or as a related pattern. Links follow the typed catalog namespace.

Built directly on (3)

Also references 9 related abstractions

Variants

Narrower or domain-specific specializations that share this archetype's core structure. Recognized variants are established; candidate variants are provisional.

One-way Sensitivity Protocol · parameter isolation variant · recognized

Test conclusion fragility by varying one uncertain parameter at a time while holding the rest of the baseline fixed.

Threshold Sensitivity Analysis · decision flip variant · recognized

Identify the parameter value or condition at which a decision, ranking, constraint, or recommendation changes.

Scenario Sensitivity Protocol · scenario bundle variant · recognized

Test a conclusion against coherent bundles of assumptions rather than varying inputs independently.

Probabilistic Sensitivity Protocol · uncertainty distribution variant · recognized

Represent uncertain inputs probabilistically and estimate how often the conclusion changes under sampled combinations.