What decision does “Scientific Due Diligence: A Falsifier-First Guide” help make?

It supports a bounded decision about which scientific claims materially affect a funding, partnership, or development decision. The framework keeps alternatives, evidence, counterevidence, uncertainty, and the fastest falsification test visible.

Can computation validate the final scientific claim?

No. This is not legal, investment, regulatory, accounting, or patent advice. Computation can prioritize and eliminate directions; final validation remains with the appropriate domain methods and accountable specialists.

When should the project stop or reframe?

Stop or reframe when no plausible outcome changes the allocation decision, critical evidence is unavailable, the thesis depends on untestable assumptions, or a cheaper route dominates on information gained per unit of cost and time.

R&D Decisions

Scientific Due Diligence: A Falsifier-First Guide

Published 2026-08-22 · Updated 2026-08-22

Answer in brief

The practical question behind Scientific Due Diligence is which scientific claims materially affect a funding, partnership, or development decision. Rank credible alternatives with claim provenance, methods, data access, reproducibility, alternative explanations, and missing validation, expose the strongest counterargument, and challenge the leader by trying to construct the strongest evidence-based case against the thesis and test whether the decision changes. A useful answer changes the next allocation decision without pretending computation is final proof.

Evidence status: Decision-method guide; not a completed investigation or final validation.

The decision this guide supports

which scientific claims materially affect a funding, partnership, or development decision

Why the problem is difficult

The article-specific identification challenge is whether the question “which scientific claims materially affect a funding, partnership, or development decision” can be resolved using claim provenance, methods, data access, reproducibility, alternative explanations, and missing validation, rather than merely restated in new language.

A falsifier-first workflow

Define the decision precisely: which scientific claims materially affect a funding, partnership, or development decision.
Build a source and data ledger around claim provenance, methods, data access, reproducibility, alternative explanations, and missing validation.
Compare the inherited route with a mechanistically distinct alternative and a constraint-based null.
Actively search for the strongest counterevidence relevant to this decision, including boundary cases and prior failures.
Run the lowest-cost discriminating challenge: construct the strongest evidence-based case against the thesis and test whether the decision changes.
Record pursue, reframe, or stop, the confidence level, the evidence ceiling, and who owns downstream validation.

Decision criteria

Decision impact: would the result materially change the choice about which scientific claims materially affect a funding, partnership, or development decision?
Evidence fit: does the available evidence—claim provenance, methods, data access, reproducibility, alternative explanations, and missing validation—directly address the decision rather than merely correlate with it?
Discrimination: does the preferred route predict an outcome a credible alternative does not?
Robustness: does the ranking survive the challenge “construct the strongest evidence-based case against the thesis and test whether the decision changes”?
Validation boundary: is the conclusion no stronger than the available sources, data and computation?

Supporting evidence

claim provenance, methods, data access, reproducibility, alternative explanations, and missing validation

Counterevidence

For this decision, a result from “construct the strongest evidence-based case against the thesis and test whether the decision changes” that reverses or flattens the ranking must remain visible even when it is commercially inconvenient.

Computation

Here computation earns its place only if it changes the choice about which scientific claims materially affect a funding, partnership, or development decision or exposes why the available evidence cannot resolve it.

Fastest falsifier

construct the strongest evidence-based case against the thesis and test whether the decision changes

When to stop or reframe

A decision-specific stop trigger is failure of the challenge “construct the strongest evidence-based case against the thesis and test whether the decision changes” without an independently supported alternative mechanism.

Evidence ceiling

This is not legal, investment, regulatory, accounting, or patent advice.

Sources and starting points

NASA Systems Engineering Handbook — Official systems-engineering guidance on requirements, verification, risk, and technical decisions.
NIST: Guidelines for Evaluating and Expressing Measurement Uncertainty — A reference for uncertainty-aware decision records.
GAO Technology Readiness Assessment Guide — Additional authoritative starting point selected for this decision area; applicability must be checked against the precise question.
NIST Data Repository — Additional authoritative starting point selected for this decision area; applicability must be checked against the precise question.

Continue the decision journey

Explore the full topic hub · Editorial standard · Scientific Oracle consulting

Frequently asked questions

What decision does “Scientific Due Diligence: A Falsifier-First Guide” help make?: It supports a bounded decision about which scientific claims materially affect a funding, partnership, or development decision. The framework keeps alternatives, evidence, counterevidence, uncertainty, and the fastest falsification test visible.
What is the fastest useful test?: construct the strongest evidence-based case against the thesis and test whether the decision changes
Can computation validate the final scientific claim?: No. This is not legal, investment, regulatory, accounting, or patent advice. Computation can prioritize and eliminate directions; final validation remains with the appropriate domain methods and accountable specialists.
When should the project stop or reframe?: Stop or reframe when no plausible outcome changes the allocation decision, critical evidence is unavailable, the thesis depends on untestable assumptions, or a cheaper route dominates on information gained per unit of cost and time.