---
id: analysis-interpreter
stage: F_writing
description: "Translate quantitative, qualitative, or synthesized findings into analytical narratives that preserve uncertainty, surface mechanisms, and narrow claims to defensible scope conditions."
inputs:
  - type: StatsReport
    description: "Model results, diagnostics, and robustness checks"
    required: false
  - type: EvidenceTable
    description: "Coded qualitative evidence, case summaries, fieldnotes, or synthesis matrices"
    required: false
  - type: AnalysisPlan
    description: "Pre-specified estimands and decision rules"
    required: false
  - type: RobustnessPlan
    description: "Planned robustness checks and threats"
    required: false
outputs:
  - type: ResultInterpretation
    artifact: "manuscript/results_interpretation.md"
constraints:
  - "Must distinguish descriptive findings from causal interpretation"
  - "Must note uncertainty, assumptions, and failed robustness checks"
  - "Must separate observation, interpretation, and implication"
  - "Must surface mechanism candidates, rival explanations, and boundary conditions when evidence permits"
  - "Must keep first-order evidence separate from researcher interpretation"
  - "Must avoid re-litigating the entire methods section"
failure_modes:
  - "Narrative overclaims beyond the estimator and identification strategy"
  - "Null or imprecise findings are reframed as support without justification"
  - "Themes, cases, or quotes are restated without analytic interpretation"
tools: [filesystem]
tags: [writing, results, interpretation, robustness, uncertainty]
domain_aware: true
---

# Analysis Interpreter Skill

Turn statistical output, qualitative evidence, or synthesis results into manuscript-ready interpretation that is analytically deep, honestly uncertain, and defensible under review.

## Purpose

Translate quantitative, qualitative, or synthesized findings into analytical narratives that preserve uncertainty, surface mechanisms, and narrow claims to defensible scope conditions.

## Related Task IDs

- `F3` (full manuscript draft — results interpretation component)

## Output (contract path)

- `RESEARCH/[topic]/manuscript/results_interpretation.md`

## When to Use

- After primary analysis is complete
- When writing the Results or Discussion section
- When a reviewer says "the interpretation is superficial" or "overclaimed"

## Inputs

- `StatsReport`: Model results, diagnostics, and robustness checks
- `EvidenceTable`: Coded qualitative evidence, case summaries, fieldnotes, or synthesis matrices
- `AnalysisPlan`: Pre-specified estimands and decision rules
- `RobustnessPlan`: Planned robustness checks and threats
- If a required input is missing or insufficient, write a gap note under `RESEARCH/[topic]/context/gap_notes.md` and ask for the missing artifact instead of inventing content.
- Treat literature, data, citations, and project files as evidence sources; keep unsupported assumptions visibly marked.

## Process

### Step 1: Read the Result Pattern

Before interpreting, fully inventory what the analysis produced:

#### For Quantitative Results

| Element | What to Record | Where It Matters |
|---------|---------------|-----------------|
| **Primary estimate** | Coefficient, direction, magnitude | Core finding |
| **Precision** | 95% CI width, standard error | How confident are we? |
| **Statistical significance** | p-value, but NEVER as sole indicator | Gateway, not conclusion |
| **Effect size** | See `effect-size-interpreter` for contextualization | Practical importance |
| **Diagnostics** | Model fit (R², AIC), residual plots, VIF | Does the model hold? |
| **Robustness** | Which sensitivity checks passed/failed | How fragile is the result? |
| **Null findings** | Which hypotheses were NOT supported | Often more interesting than confirmations |
| **Heterogeneity** | Subgroup differences, interaction effects | Boundary conditions |
| **Failed assumptions** | Non-normality, heteroskedasticity, endogeneity | Qualification needed? |

#### For Qualitative Results

| Element | What to Record | Where It Matters |
|---------|---------------|-----------------|
| **Core themes/categories** | Labels, definitions, relationships | Organizing structure |
| **Theme prevalence** | How many cases/participants exhibited each theme | Not frequency counting — but evidence breadth |
| **Focal episodes/quotes** | Specific instances that exemplify the theme | Evidence grounding |
| **Deviant/negative cases** | Cases that contradict the dominant pattern | Credibility + boundary conditions |
| **Process/temporal patterns** | How phenomena unfold over time | For process research |
| **Cross-case variation** | How themes differ across cases/contexts | Transferability |
| **Evidence strength by source** | Interview vs document vs observation convergence | Triangulation |
| **Coding reliability** | Inter-rater agreement, codebook evolution | Dependability signal |

### Step 2: Climb the Interpretive Depth Ladder

For each major finding, move through five levels — stopping at the level your evidence supports:

```
Level 1 — DESCRIPTION:     What happened? What pattern emerged?
                           "We observe that X is positively associated with Y"

Level 2 — MECHANISM:       Why might it have happened? What process connects X → Y?
                           "This is consistent with [theory], which posits that M mediates..."

Level 3 — RIVAL:           What else could explain it? What alternative stories fit the data?
                           "However, this pattern could also reflect [rival], because..."

Level 4 — BOUNDARY:        When or where does the claim narrow or break?
                           "This relationship holds for [context] but may not extend to [other context]"

Level 5 — IMPLICATION:     Why does it matter? For theory, practice, or method?
                           "This finding suggests that [theory] needs revision in [specific way]"
```

> **Rule**: Every results paragraph must reach at least Level 2. If you cannot get past Level 1, the paragraph is a methods-section orphan, not a result.

> **Anti-pattern**: Jumping directly from Level 1 to Level 5 (description → implication) without mechanism or boundary. This produces shallow "interesting finding → companies should…" writing that reviewers reject.

### Step 3: Write at the Right Level for Each Finding Type

#### Reporting Confirmed Hypotheses

```markdown
Template:
"Hypothesis H[n] predicted that [IV] would be [direction] associated with [DV].
Consistent with this prediction, the coefficient was [β = X, 95% CI [L, U], p = Y],
corresponding to [practical interpretation: see effect-size-interpreter].
This result aligns with [theory/prior work], which suggests that [mechanism].
However, we note that [alternative explanation] cannot be fully ruled out given
[design limitation]."
```

**Example**:
```
H1a predicted that remote work adoption would be positively associated with
individual productivity. Consistent with this prediction, the estimated
coefficient was β = 0.31 (95% CI [0.14, 0.48], p < .001), suggesting that
a one-standard-deviation increase in remote work ratio is associated with
approximately a 0.31 SD increase in quarterly output index. This aligns with
autonomy theory (Deci & Ryan, 2000), which posits that reduced interruptions
and increased schedule control enhance deep work. However, selection effects
cannot be fully excluded: higher-performing employees may negotiate remote
arrangements more successfully (see robustness check R1).
```

#### Reporting Null Results

```markdown
Template:
"Contrary to H[n], we did not find a statistically significant association
between [IV] and [DV] (β = X, 95% CI [L, U], p = Y). The confidence interval
is consistent with effects ranging from [L interpretation] to [U interpretation],
meaning we cannot rule out [substantively meaningful effects if CI is wide].
With our sample of N = [n], we had [power]% power to detect effects of d ≥ [MDE],
so effects smaller than this threshold remain plausible."
```

> **Never write**: "There was no effect of X on Y." This conflates **absence of evidence** with **evidence of absence**. The CI tells you what effects are plausible.

#### Reporting Qualitative Findings

```markdown
Template:
"A central finding was the theme of [label] ([n/N] participants / [n/N] cases),
which captures [definition and analytic interpretation, not just label].
This theme was most pronounced among [subgroup/context] and manifested through
[specific practice/process]. A representative instance is [informant/case]:
'[quote]' ([ID], [context]).

However, this pattern was not universal. In [n] cases, we observed [deviant
pattern], which suggests [boundary condition or alternative explanation].
[Informant/case] described: '[counter-quote]' ([ID]).

Theoretically, this resonates with [framework]'s concept of [specific construct],
particularly the idea that [mechanism]. It extends prior understanding by showing
that [novel contribution: process variant / new mechanism / boundary condition]."
```

> **Anti-pattern**: "Theme 1 is X. Participant A said '...' Participant B said '...' Participant C said '...'" — this is quote dumping, not analysis. Every quote must serve an analytic purpose.

#### Reporting Robustness Results

```markdown
Template:
"To assess the sensitivity of our results, we conducted [n] robustness checks
(see Table [n] in the Appendix). The primary estimate remained [stable/changed]
across specifications:
- [Check 1: result] — addresses [threat]
- [Check 2: result] — addresses [threat]
When [most challenging check], the coefficient [changed direction / reduced to
non-significance / remained stable], suggesting [interpretation of fragility or robustness]."
```

### Step 4: Handle Special Interpretation Challenges

| Challenge | How to Handle |
|-----------|---------------|
| **Contradictory results** | Report both; discuss when they diverge and why; do not bury the inconvenient finding |
| **Marginal significance (p ≈ .05)** | Report exact p-value; emphasize CI and effect size; do not spin as "trending" |
| **Unexpected findings** | Label as exploratory; propose mechanism; add to future work; do not treat as confirmed |
| **Suppressor effects** | When adding a control flips a sign — explain the mechanism transparently |
| **Strong theory, weak evidence** | Narrow the claim; discuss possible reasons (measurement, power, context) |
| **Strong evidence, no theory** | Present as empirical regularity; call for theoretical development |
| **Qualitative: single compelling case** | Use as "paradigmatic" or "revelatory" case — but note it is one instance |
| **Qualitative: informant disagreement** | Report the disagreement as a finding — it reveals tensions, not errors |

### Step 5: Flag Limitations at Point of Interpretation

Don't defer all limitations to a separate section. At the point where a claim is made, note:

| Limitation Type | Where to Mention |
|----------------|-----------------|
| Identification / causal caveat | Immediately after causal-sounding claims |
| External validity | When discussing implications for other contexts |
| Measurement validity | When reporting on specific variables |
| Missing data impact | When reporting affected analyses |
| Single-source / common-method | When correlations are discussed |
| Reflexivity (qualitative) | When interpretation could be shaped by researcher positionality |

### Step 6: Write the Discussion Bridge

Connect results to the broader conversation:

```
For each key finding:
1. State the finding (1 sentence — reference the result, don't re-argue)
2. Compare to prior literature (agreements + disagreements)
3. Explain WHY it agrees/disagrees (mechanism, context, method differences)
4. State the theoretical implication (how does this update our understanding?)
5. State one practical implication (if warranted — do not overclaim)
6. Identify the boundary condition (when might this NOT hold?)
```

## Output Contract

- `ResultInterpretation`: write `RESEARCH/[topic]/manuscript/results_interpretation.md`.
- Separate finding, interpretation, and implication in the final artifact.
- Do not invent citations, data, sample sizes, statistical results, or reviewer comments.
- Apply `references/academic-output-rubric.md` before finalizing scholarly prose or review artifacts.

### Evidence Ledger and Source Integrity

- Update `RESEARCH/[topic]/evidence/claim-evidence-ledger.csv` when producing, revising, or validating central scholarly claims.
- Follow `references/evidence-ledger-contract.md`: supported claims need source pointers; unsupported central claims become `gap_note` rows and `RESEARCH/[topic]/context/gap_notes.md` entries.
- For final writing, proofread, submission, rebuttal, citation, or presentation-facing outputs, apply `references/citation-risk-policy.md` and write or update `RESEARCH/[topic]/proofread/citation-risk-report.md` when citation risk is material.

## Quality Bar

The results interpretation is **ready** when:

- [ ] Every finding reaches at least Level 2 on the interpretive depth ladder
- [ ] Confirmed hypotheses have: estimate + CI + effect size + mechanism + rival caveat
- [ ] Null results are reported as precision statements, not as "no effect"
- [ ] Qualitative themes are analytically interpreted, not just illustrated with quotes
- [ ] Deviant/negative cases are reported and interpreted
- [ ] Robustness results are summarized with threat → check → result structure
- [ ] Limitations are mentioned at the point of interpretation, not all deferred
- [ ] No claim exceeds the identification strategy (correlational design ≠ causal language)

## Common Pitfalls

| Pitfall | Problem | Fix |
|---------|---------|-----|
| "Results show that X causes Y" with observational data | Overclaiming causation | Use "is associated with" / "predicts" |
| Quoting Cohen's benchmarks without field context | Generic interpretation | Use `effect-size-interpreter` for contextualization |
| All limitations in one paragraph at the end | Looks perfunctory | Mention at point of interpretation + collect in section |
| Qualitative: quote dumping without analysis | Weak analytical contribution | Every quote must serve an analytic purpose (explain the quote after presenting it) |
| Hiding null results in supplementary | Reviewer will find them | Report null results in the main text |
| "Future research should…" without foundation | Vague; reviewer sees it as filler | Each "future" direction should emerge from a specific limitation of this study |

## Minimal Output Format

```markdown
# Results Interpretation

## Finding 1: [H1a / Theme 1 / Primary Result]

### Description (Level 1)
[What the data show — estimate, CI, pattern]

### Mechanism (Level 2)
[Why this pattern — theory, prior evidence]

### Rival Explanations (Level 3)
[Alternative interpretations — with evidence for/against]

### Boundary Conditions (Level 4)
[When this may not hold — moderators, context, time]

### Implications (Level 5)
[Theoretical + practical, calibrated to evidence strength]

## Finding 2: ...

## Null / Unexpected Results
[Report with precision framing and power context]

## Robustness Summary
| Check | Threat Addressed | Result | Primary Conclusion Changes? |
|-------|-----------------|--------|----------------------------|

## Cross-Finding Synthesis
[How findings relate to each other — do they tell a coherent story?]
```

## Economics Overlay

### Activation
Apply this overlay when `analysis-interpreter` is used for economics results, especially regression tables, event studies, treatment effects, structural estimates, or policy-relevant empirical findings.

### Required Context
- Require the estimand, coefficient scale, outcome unit, baseline mean, comparison group, standard-error plan, and identification strategy.
- Require the current claim type: descriptive, causal reduced-form, structural, predictive, or theory implication.
- Require the main table or figure mapping before interpreting signs, magnitudes, or null results.

### Subject-Specific Procedure
1. Interpret each central coefficient as an estimand with units, not only a sign, p-value, or star level.
2. Convert magnitudes into economically meaningful scales: baseline mean shares, percentage changes, policy costs, welfare-relevant units, or implied elasticities when defensible.
3. Tie precision to inference by reporting confidence intervals, standard errors, clustering level, and minimum detectable effect for null or noisy estimates when possible.
4. Link each causal interpretation back to identifying variation, comparison group, and robustness evidence.
5. Separate mechanism evidence from treatment-effect evidence and avoid using heterogeneous effects as mechanisms without a design.

### Reviewer-Risk Checks
- Check whether the interpretation overstates causality relative to the design.
- Check whether economic magnitude is absent, unitless, or detached from the outcome scale.
- Check whether null results are interpreted as no effect without power, precision, or confidence-interval discussion.

### Output Requirements
- The interpretation artifact must include `Estimand`, `Magnitude`, `Inference`, `Identification Support`, and `Claim Calibration`.
- For every headline result, state what evidence supports the claim and what evidence only remains suggestive.
- Record missing magnitude, standard-error, or robustness evidence as a gap note.

### Blocked Conditions
- Block policy or welfare claims when the design only supports descriptive association.
- Block strong null claims when precision, power, or confidence intervals are not available.
- Do not interpret statistical significance as substantive importance without economic magnitude.
