inductive-hypothesis-generation

name	inductive-hypothesis-generation
description	Strategy: induce and distill hypotheses from data/observations
version	1.0.0
category	hypothesis-formation
type	strategy
campaign	hypothesis-formulation
tactics	["anomaly-driven-abduction","falsifiability-audit"]
sops	["anomaly-characterization","explanation-generation","variable-identification","relationship-specification","falsifiability-check"]
dependencies	{"tactics":["anomaly-driven-abduction","falsifiability-audit"],"sops":["hypothesis-formation-variable-identification","relationship-specification"]}

Inductive Hypothesis Generation

Induce and distill hypotheses from data/observations: in domains with theoretical gaps or insufficient theory, distill regularities from empirical patterns and cautiously generalize them into testable propositions.

When to Use

• The domain lacks mature theory but has accumulated abundant empirical observations or data patterns
• The research gap appears as "a recurring phenomenon with no systematic explanation yet"
• The goal is to distill regularities from data, laying a foundation for subsequent theory construction
• An exploratory research stage where it is not yet clear which variables matter

Not applicable: domains that already have a clear theoretical framework → use deductive-hypothesis-generation instead.

Thinking Framework

Observe patterns → Extract regularity → Generalize cautiously → Formulate testable claim

The core logic of induction:

1. Observe patterns: systematically organize patterns that recur across existing observations, data, and cases (not single anomalies)
2. Extract regularity: identify the regularity behind the patterns — under what conditions it appears, under what conditions it does not
3. Generalize cautiously: cautiously generalize the regularity from the specific samples — make the boundary of generalization explicit, do not over-extrapolate
4. Formulate testable claim: turn the generalized regularity into a proposition that can be tested on new samples

The core risk of induction: over-generalization (jumping from a limited sample to a universal law). Each inductive hypothesis must make explicit:

• Which samples the observations come from (sample characteristics, source, time range)
• Which population it generalizes to (the boundary of generalization scope)
• What evidence would limit or refute the generalization

Budget Gate

Tier	Pattern coverage	Regularity extraction	Hypothesis yield	Generalization boundary
S	≥3 independent observation patterns	≥2 regularities	≥2 structured hypotheses	Each hypothesis specifies its sample source
M	≥5 independent observation patterns	≥3 regularities	≥3 structured hypotheses	Generalization boundary + falsification scenario