Scientific Hypothesis Generation

Turn an observation into a transparent set of candidate explanations and tests. A hypothesis is a proposal to be challenged, not a finding, fact, diagnosis, or recommendation.

Non-negotiable boundaries

Before using unpublished, sensitive, controlled, personal, proprietary, export-controlled, or security-relevant material:

1. Confirm authorization and the applicable institutional, funder, publisher, data-use, privacy, and AI policies.
2. Keep the material local unless an authorized human explicitly approves a named external destination and data scope.
3. Minimize inputs. Do not place sensitive or unpublished data in web searches or external AI systems without authorization.
4. Stop at the appropriate human, animal, biosafety, dual-use, data-governance, or regulatory gate.

Never:

• present a hypothesis, mechanism, causal effect, citation, or apparent pattern as established evidence;
• claim novelty because a quick search found nothing;
• infer causation from association, temporal order alone, predictive accuracy, or model output;
• supply patient-specific diagnosis, treatment, dose, prognosis, or other clinical advice;
• provide harmful experimental optimization or operational detail for pathogens, toxins, weapons, evasion, or other misuse;
• bypass IRB/REC, IACUC, IBC, biosafety, dual-use, privacy, legal, or regulatory review;
• fabricate sources, identifiers, search coverage, data, results, approvals, or preregistration;
• automatically score, rank, select, accept, or reject scientific hypotheses.

If a request crosses a safety gate, produce only a high-level risk/oversight note and route it to the qualified local authority. Do not continue with operational detail.

Keep the objects distinct

Do not collapse these labels. A mechanistic story is not a prediction; a prediction is not evidence; rejection of one null does not prove a mechanism; support for one candidate does not eliminate unconsidered rivals.

Workflow

1. Run the scope and safety gate

Record:

• accountable human owner and intended use;
• data sensitivity, authorization, retention, and permitted processing;
• affected people, animals, ecosystems, communities, or security interests;
• required ethics, feasibility, biosafety, dual-use, and regulatory reviews;
• unresolved blocks and domain expertise needed.

No script approval is an ethics, safety, regulatory, or scientific approval.

2. Freeze the observation

Write the observation before interpretation:

• measurement or source;
• population, system, place, and time;
• unit of observation and unit of analysis;
• uncertainty, missingness, exclusions, and preprocessing;
• whether the pattern was expected, exploratory, or selected after viewing results.

Use “reported,” “observed,” or “associated,” not causal language, unless a causal design and estimand justify it.

3. Frame the research question

Choose a framework only when it fits:

• PICO/PICOT for intervention/effectiveness questions: population, intervention, comparator, outcome, and optionally time.
• PECO for exposure questions.
• Population–index test–reference standard–target condition for diagnostic accuracy.
• Population–prognostic factor–outcome–time for prognosis.
• A domain-specific construct–context–outcome frame for qualitative, descriptive, mechanistic, or theoretical work.

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