skills/hypothesis-generation/references/concepts_and_workflow.md
This reference prevents common category errors in hypothesis work. It is a vocabulary and workflow guide, not a theory of confirmation and not an automatic ranking method.
A bounded account of what was detected or reported:
An observation can be mistaken, biased, or unrepresentative. It does not explain itself.
An answerable question that fixes the scope of inquiry. It should identify the target population/system, variables or interventions, comparator where meaningful, outcome, timeframe, context, and claim type.
PICO/PICOT is appropriate for many intervention-effect questions. It is not a universal ontology. Use a framework matched to the question and involve affected stakeholders where appropriate.
A candidate proposition that could explain or relate observations and yield testable implications. Keep its status as candidate until evidence changes the state. Avoid “validated hypothesis,” “proven mechanism,” and similar language unless the statement is being used only to quote a source accurately.
A proposed process connecting antecedent conditions to an outcome. A mechanism should identify entities, activities, ordering, and boundary conditions where the domain permits. A plausible narrative without discriminating predictions remains a story.
A precise target causal contrast. At minimum, state:
The estimand is the target, the estimator is the method, and the estimate is the numerical result.
An observable implication derived from a candidate before checking the target result. A useful prediction specifies conditions, measurement, expected pattern, uncertainty, and an incompatible result. It should distinguish at least one rival when possible.
A rival account that could produce the same observation. Rivals include:
Rivals can coexist. Do not force mutual exclusivity when a mixed explanation is scientifically plausible.
A defined no-effect/no-difference model used in an analysis. It is not “nothing happened,” and failure to reject it does not establish equivalence or absence. Define compatibility, equivalence, or non-inferiority rules separately when those are the scientific targets.
A control in which the target mechanism should not operate but relevant bias pathways should remain. Negative exposure and negative outcome controls can reveal confounding, selection, measurement, or analytic bias when their assumptions are credible. A negative control does not repair bias automatically.
The mapping from a construct to a measurement, category, intervention, or variable. Record instrument/method, unit, timing, population/system, validity, reliability, calibration, missingness, transformations, cut points, and limitations.
The planned mapping from data to estimand, prediction, or descriptive target. It includes units, populations, transformations, models, contrasts, effect measures, uncertainty, missingness, multiplicity, diagnostics, sensitivity analyses, and decision rules.
Empirical observations or documented sources that bear on claims. Record whether a source supports, challenges, contextualizes, or supplies a method. Citation presence does not prove claim support; a human must inspect the source.
Use explicit states:
Never use true, proven, or selected_winner as a machine-generated state.
Platt’s 1964 strong-inference essay advocates:
Use this as a discipline for contrast, not as a guarantee of truth. In practice:
Always include an “unknown or mixed explanation” path in interpretation.
Both modes are scientifically valuable. The integrity failure is not exploration; it is presenting exploration as if it were prespecified.
Prefer:
Avoid:
A hypothesis package should contain: