inductive-reasoning

name	inductive-reasoning
description	Use when patterns, trends, generalizations, or likely rules must be inferred from examples, cases, observations, data points, interviews, events, or repeated signals. Use when applying inductive reasoning, pattern inference, evidence-to-generalization synthesis, or trend interpretation.
license	Apache-2.0

Inductive Reasoning

Use this skill to infer a likely pattern or general rule from observed evidence.

Method Notes

• Inductive reasoning moves from specific observations to a broader pattern or generalization.
• Inductive conclusions are probabilistic, not certain.
• The method should make sample size, representativeness, exceptions, and confidence visible.
• It is useful for customer research, repeated stakeholder behavior, market signals, operational symptoms, and trend interpretation.

Required Inputs

Collect or infer these inputs before execution:

• observations, examples, cases, data points, quotes, or events
• source and timing of observations
• population or context being generalized to
• known exceptions
• decision or hypothesis the pattern will inform

If an input is missing, mark it as missing, state the assumption used, and add a validation action.

When Not To Use

Do not use when the conclusion must follow from formal rules or fixed criteria. Use deductive-reasoning instead.

Step-by-Step Execution

Step	Required input	How to execute	Output
Define the inference question	Decision context, target population.	State what pattern or generalization is being tested.	Inference question.
List observations	Cases, data points, quotes, events.	Put each observation in a separate row with source and timing.	Observation table.
Cluster patterns	Observation table.	Identify repeated behaviors, causes, needs, outcomes, or signals.	Pattern candidates.
Test strength	Sample size, representativeness, consistency.	Rate each pattern by frequency, diversity of support, and evidence quality.	Pattern strength table.
Look for exceptions	Known counterexamples, missing segments.	Identify observations that contradict or limit the pattern.	Exception list.
State likely generalization	Pattern strength and exceptions.	Write the conclusion with confidence and boundary conditions.	Inductive conclusion.
Define validation	Weakest evidence gaps.	Specify what additional evidence would strengthen or weaken the pattern.	Validation plan.

Output Template

### 1. Inference Question
- What we are trying to infer:
- Population / context:
- Decision use:

### 2. Observation Table
| Observation | Source | Timing | Supports which pattern | Notes |
|---|---|---|---|---|
|  |  |  |  |  |

### 3. Pattern Candidates
| Pattern | Supporting observations | Exceptions | Confidence | Boundary |
|---|---|---|---|---|
|  |  |  | high / medium / low |  |

### 4. Inductive Conclusion
- Likely pattern:
- What it does not prove:
- What would strengthen it:
- What would weaken it:

Quality Gate

• Do not overgeneralize from a thin or biased sample.
• Look for exceptions before writing the conclusion.
• State confidence as probability, not certainty.
• Keep the boundary of the generalization explicit.
• Preserve the link between conclusion and observations.