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Method & Guide · evidence connected

Separate observations from inference

Turn sparse results into a decision without presenting labels, causes, or forecasts as observed facts.

10–15 minRichBay testedReviewed 2026-09-06
← All Methods & GuidesStart the method

Use when

You are interpreting pilot metrics, interviews, experiments, research notes, or AI-generated summaries that can easily overstate what the evidence shows.

What you will produce

An observation-versus-inference ledger, a bounded decision, and one concrete evidence-gathering step.

On this pageStepsDecision rulesReusable templateLinked evidence
RichBay tested

RichBay tested this ledger against the published pilot and conversion task packets. It makes unsupported labels visible; it does not supply a missing business benchmark or causal design.

Step by step

Apply the method to one real task.

01

Copy observations without adjectives

Record counts, dates, quoted statements, and measured rates exactly as supplied. Remove labels such as good, low, strong, representative, or successful unless the packet defines them.

Checkpoint
Each observation can be traced to a visible source and does not contain an unstated judgment.
02

Place interpretations in a separate column

Write every explanation, label, generalization, causal claim, and forecast as an inference. Name which observation motivated it without treating that link as proof.

Checkpoint
A reader can distinguish what happened from what the reviewer thinks it may mean.
03

Expose the comparison rule

For labels such as high, low, ready, or improved, record the benchmark, target, comparison cohort, or decision threshold. If none exists, mark the label unsupported.

Checkpoint
Every evaluative label has a documented rule or is removed from the decision.
04

Choose only among supported actions

State what the current evidence permits: proceed within a boundary, pause for evidence, stop, or remain unresolved. Do not invent a forced answer when the decision rule is missing.

Checkpoint
The action follows the ledger and includes the human owner of the decision.
05

Name the next discriminating evidence

Ask for the smallest new observation that could change the action: a retention window, representative sample, concurrent randomized test, target, or failure-rate check.

Checkpoint
The next step reduces a named uncertainty rather than collecting more undirected data.

Decision rules

Make the boundary explicit.

  • Counts are observations; labels such as low or strong require a benchmark.
  • Sequential changes are signals, not causal proof, unless the design controls relevant alternatives.
  • A sparse packet can support a bounded next step even when it cannot support expansion or attribution.
  • State unresolved when the evidence cannot distinguish the available decisions.

Reusable artifact

Observation–inference ledger

Copy the template into your notes, or download a Markdown file and keep it with the task evidence.

Preview the template
# Observation–Inference Ledger

## Decision
- Decision to make:
- Available actions:
- Decision owner:

## Ledger
| Observation (source wording) | Source | Inference or label | Required benchmark / assumption | Supported? |
| --- | --- | --- | --- | --- |
|  |  |  |  | Yes / No / Unclear |

## Bounded conclusion
- What the evidence directly supports:
- What remains inference:
- Current action:
- Why this action fits the boundary:

## Next evidence
- Uncertainty to reduce:
- Smallest discriminating observation or test:
- What result would change the action:

Linked evidence

Inspect where this method was applied.

  1. Pilot evidence Reviewed Case

    The source case for separating invite, activity, and interview counts from readiness labels.

  2. Conversion uncertainty Reviewed Case

    The source case for separating observed rate changes from causal attribution.

Continue the loop

Practice, inspect, or build with the result.

Try the pilot ChallengeCompare conversion outputsRead Human Oversight