frumu-ai Validate Analysis
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Review an analysis for accuracy, methodology, and potential biases before sharing with stakeholders. Generates a confidence assessment and improvement suggestions.
Usage
You can ask to validate an analysis (e.g., "Validate this report" or "Check my query logic").
Arguments
analysis to review— A document, report, SQL query, chart, or description of methodology
Workflow
1. Review Methodology and Assumptions
Examine:
- Question framing: Is the analysis answering the right question? Could the question be interpreted differently?
- Data selection: Are the right tables/datasets being used? Is the time range appropriate?
- Population definition: Is the analysis population correctly defined? Are there unintended exclusions?
- Metric definitions: Are metrics defined clearly and consistently? Do they match how stakeholders understand them?
- Baseline and comparison: Is the comparison fair? Are time periods, cohort sizes, and contexts comparable?
2. Check for Common Analytical Errors
Systematically review for:
Data completeness:
- Missing data that could skew results (e.g., nulls in key fields, missing time periods)
- Data freshness issues (is the most recent data actually complete or still loading?)
- Survivorship bias (are you only looking at entities that "survived" to the analysis date?)
Statistical issues:
- Simpson's paradox (trend reverses when data is aggregated vs. segmented)
- Correlation presented as causation without supporting evidence
- Small sample sizes leading to unreliable conclusions
- Outliers disproportionately affecting averages (should medians be used instead?)
- Multiple testing / cherry-picking significant results
Aggregation errors:
- Double-counting from improper joins (many-to-many explosions)
- Incorrect denominators in rate calculations
- Mixing granularity levels (e.g., user-level metrics averaged with account-level)
- Revenue recognized vs. billed vs. collected confusion
Time-related issues:
- Seasonality not accounted for in comparisons
- Incomplete periods included in averages (e.g., partial month compared to full months)
- Timezone inconsistencies between data sources
- Look-ahead bias (using future information to explain past events)
Selection and scope:
- Cherry-picked time ranges that favor a particular narrative
- Excluded segments without justification
- Changing definitions mid-analysis
3. Verify Calculations and Aggregations
Where possible, spot-check:
- Recalculate a few key numbers independently
- Verify that subtotals sum to totals
- Check that percentages sum to 100% (or close to it) where expected
- Confirm that YoY/MoM comparisons use the correct base periods
- Validate that filters are applied consistently across all metrics
4. Assess Visualizations
If the analysis includes charts:
- Do axes start at appropriate values (zero for bar charts)?
- Are scales consistent across comparison charts?
- Do chart titles accurately describe what's shown?
- Could the visualization mislead a quick reader?
- Are there truncated axes, inconsistent intervals, or 3D effects that distort perception?
5. Evaluate Narrative and Conclusions
Review whether:
- Conclusions are supported by the data shown
- Alternative explanations are acknowledged
- Uncertainty is communicated appropriately
- Recommendations follow logically from findings
- The level of confidence matches the strength of evidence
6. Suggest Improvements
Provide specific, actionable suggestions:
- Additional analyses that would strengthen the conclusions
- Caveats or limitations that should be noted
- Better visualizations or framings for key points
- Missing context that stakeholders would want
7. Generate Confidence Assessment
Rate the analysis on a 3-level scale:
Ready to share -- Analysis is methodologically sound, calculations verified, caveats noted. Minor suggestions for improvement but nothing blocking.
Share with noted caveats -- Analysis is largely correct but has specific limitations or assumptions that must be communicated to stakeholders. List the required caveats.
Needs revision -- Found specific errors, methodological issues, or missing analyses that should be addressed before sharing. List the required changes with priority order.
Output Format
## Validation Report
### Overall Assessment: [Ready to share | Share with caveats | Needs revision]
### Methodology Review
[Findings about approach, data selection, definitions]
### Issues Found
1. [Severity: High/Medium/Low] [Issue description and impact]
2. ...
### Calculation Spot-Checks
- [Metric]: [Verified / Discrepancy found]
- ...
### Visualization Review
[Any issues with charts or visual presentation]
### Suggested Improvements
1. [Improvement and why it matters]
2. ...
### Required Caveats for Stakeholders
- [Caveat that must be communicated]
- ...
Tips
- Run validation before any high-stakes presentation or decision
- Even quick analyses benefit from a sanity check -- it takes a minute and can save your credibility
- If the validation finds issues, fix them and re-validate
- Share the validation output alongside your analysis to build stakeholder confidence