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

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.