analyze-experiments

Experiment Analyst

Perform comprehensive, detailed deep-dive analysis of experiments to make data-driven ship/no-ship decisions. This is NOT a quick summary - provide thorough insights with specific numbers and business implications.

When to Use

• Analyzing completed experiment results for ship decisions
• Checking on running experiment progress and early signals
• Understanding why an experiment succeeded or failed
• Investigating unexpected results or segment-level effects

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Analysis Philosophy

Be comprehensive, not brief:

• Include specific numbers, percentages, and data points
• Explain statistical meaning AND business implications in plain language
• Cover all metrics (primary, secondary, guardrails) with actual values
• This is a single comprehensive analysis - do not rush or provide superficial summaries

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Instructions

Step 0: Identify Experiment

If user provides a specific experiment:

• Accept experiment URL or experiment ID
• If URL: use Amplitude:get_from_url to extract details
• If ID: proceed to Step 1

If user asks about experiments generally:

• Use Amplitude:search with entityTypes: ["EXPERIMENT"] and relevant query terms
• Present top 3-5 matches with names, IDs, and states
• Ask user which experiment to analyze

If no experiment specified:

• Ask explicitly for experiment URL, ID, or search terms and stop

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Step 1: Retrieve and Validate Setup

Use Amplitude:get_experiments with experiment ID to capture:

• Experiment name, key, description, and state
• Start/end dates and duration
• Variants: names, traffic allocation
• Attached metrics: primary (recommendation=true), secondary, guardrails (stores as IDs)
• Bucketing strategy

Get metric names:

• Extract metric IDs from the experiment response (e.g., "c4pn8fkv")
• CRITICAL: Amplitude MCP cannot retrieve metric names by ID directly
• Workaround options:
  1. Search for experiment-related charts using Amplitude:search with entityTypes: ["CHART"] and experiment name
  2. Use Amplitude:get_charts on related charts to examine their definitions for metric references
  3. Check if experiment description contains links to metric documentation
• If metric names cannot be found, report as descriptive placeholders:
  • Primary metric: "Primary Goal Metric (ID: {id})"
  • Secondary metrics: "Secondary Metric {index} (ID: {id})"
  • Include metric IDs so users can look them up in Amplitude UI

Validation:

• Is experiment running or completed? (not draft)
• Has it run for 1+ weeks?
• Are variants and metrics clearly defined?

If incomplete, explain what's missing and stop.

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Step 2: Check Data Quality (with explicit thresholds)

Use Amplitude:query_experiment (primary metric only) to assess:

Traffic Balance (SRM Check):

• Report actual traffic split per variant (e.g., 48.2% control, 51.8% treatment)
• Use srmDetected field from API: Flag if srmDetected: true
• SRM (Sample Ratio Mismatch) indicates the observed traffic split deviates significantly from the expected allocation
• If SRM detected, report the expected vs. actual allocation with specific percentages
• Severe SRM can indicate instrumentation issues or bucketing problems that may invalidate results

Sample Size Analysis:

A. Current Sample Assessment:

• Report total users per variant with specific numbers
• Flag if <100 users per variant (insufficient for any conclusion)
• Flag if 100-1000 users (directional signals only, not confident decision)
• Need 1000+ per variant for confident decisions

B. Statistical Power Analysis:

• Target effect size: What minimum lift would be meaningful for the business? (typically 2-5% for conversion metrics)
• Achieved power: Given current sample size and observed variance, what's the probability of detecting the target effect if it exists?
• Power interpretation:
  • <50%: Severely underpowered - likely to miss real effects
  • 50-70%: Underpowered - high risk of false negatives
  • 70-80%: Marginally adequate - consider extending if p-value is borderline
  • 80%+: Well-powered - sufficient to detect target effect size
• If underpowered: Calculate additional sample size needed to reach 80% power
• Recommendation: If power <70% and results are inconclusive, extend duration rather than making premature decision

C. Precision Analysis (Confidence Interval Width):

• CI width for primary metric: Report the width of the 95% confidence interval as percentage of baseline
• Precision assessment:
  • CI width >10% of baseline: Low precision - effect size uncertainty too high for confident decisions
  • CI width 5-10% of baseline: Moderate precision - acceptable for directional decisions
  • CI width <5% of baseline: High precision - narrow enough for confident decisions
• Actionability threshold: Is the CI narrow enough to distinguish between practically significant and negligible effects?
  • If lower CI bound suggests meaningful lift but upper bound is marginal, precision may be insufficient
  • Example: If target is +5% lift and CI is [-2%, +12%], too wide to confidently conclude effect exceeds target
• Recommendation: If CI too wide, extend duration or increase traffic allocation to improve precision

Comprehensive Data Quality Flags:

The Amplitude:query_experiment API returns multiple boolean flags that assess statistical validity. Check and document each:

1. statsAssumptionsMetForWholeExperiment:

   • Indicates whether core statistical assumptions are satisfied (normality, independence)
   • If false: Results may not be reliable; consider non-parametric approaches or longer runtime
   • Impact: High - affects all statistical conclusions

2. hasSuspiciousUplift:

   • Flags unexpectedly large effect sizes that may indicate data quality issues
   • If true: Verify instrumentation, check for bot traffic, or segment anomalies
   • Impact: High - may indicate measurement error rather than real effect

3. isVariancePositive:

   • Confirms metric variance is positive (mathematically required for statistical tests)
   • If false: Critical data quality issue - metric may be constant or incorrectly computed
   • Impact: Critical - statistical tests invalid if false

4. isConfidenceIntervalNotFlipped:

   • Ensures lower CI bound < upper CI bound (mathematical consistency check)
   • If false: Indicates calculation error or data corruption
   • Impact: Critical - results cannot be trusted

5. isStandardErrorLargeEnough:

   • Checks if standard error is sufficient for reliable inference
   • If false: High variance or very small sample may produce unreliable confidence intervals
   • Impact: Medium - affects precision of estimates

6. isPointEstimateInsideConfidenceInterval:

   • Validates that point estimate falls within its confidence interval (consistency check)
   • If false: Calculation error or numerical instability
   • Impact: High - indicates statistical computation issues

7. isMeanValid:

   • Confirms mean value is a valid number (not NaN, not infinite)
   • If false: Data quality issue - check for null values or computation errors
   • Impact: Critical - cannot analyze if mean is invalid

For each flag that fails (returns false or true for suspicious uplift), document:

• Which flag failed
• What it means in plain language
• Specific impact on result reliability
• Recommended action (extend duration, investigate instrumentation, etc.)

If all flags pass: Note this explicitly as strong data quality signal

Temporal Stability:

• Check if primary metric is stable day-over-day
• Note ramp period (first 24-48hrs) or day-of-week effects

Document all data quality issues found - these affect result reliability.

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Step 3: Analyze Primary Metric

Use Amplitude:query_experiment without metricIds to get primary metric only.

Use metric name from Step 1 - Report using the human-readable metric name, not the metric ID.

Extract and report:

• Control baseline: metric value and sample size
• Treatment performance: metric value and sample size
• Absolute lift: treatment - control
• Relative lift: (treatment - control) / control × 100%
• P-value: with interpretation
• Confidence interval: report 95% CI bounds

Interpret:

• ✅ Statistically significant: p < 0.05 and CI doesn't include 0
• ⚠️ Trending: 0.05 < p < 0.15 (suggestive but inconclusive)
• ❌ No effect: p ≥ 0.15 or CI includes 0

Practical significance:

• Is the lift magnitude meaningful for the business?
• Small lifts (<2-3%) may not be worth complexity even if significant
• Consider metric's business impact (revenue vs. low-value engagement)

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Step 4: Analyze Secondary Metrics & Guardrails

Use Amplitude:query_experiment with metricIds for all metrics.

Use metric names from Step 1 - Report using human-readable metric names, not metric IDs.

For each secondary metric:

• Report metric name (from Step 1 mapping), variant performance, and statistical significance
• Note which moved and which didn't (with specific numbers)
• Identify unintended consequences: Flag any negative impacts with specific values

For each guardrail:

• ✅ No regression: neutral or positive (p > 0.05)
• ⚠️ Marginal concern: small negative lift (1-5%) with p < 0.10
• 🚩 Significant regression: negative lift with p < 0.05 - report actual numbers

Key question: Are any metrics showing degradation (revenue, retention, engagement, error rates)?

Multiple testing: If analyzing 5+ metrics, consider Bonferroni correction (alpha = 0.05 / number of metrics)

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Step 5: Comprehensive Segment Analysis

Use Amplitude:query_experiment with groupBy parameter (one at a time).

Test 3-4 high-signal segments:

1. Platform (iOS, Android, Web)
2. User tenure (new vs. established users)
3. Plan type (free vs. paid)
4. Geography (country, region)

MANDATORY: Format results as markdown breakdown tables

For each segment analysis, present results in this exact format:

Segment	Control Rate	Control Exposures	Control % of Total	Treatment Rate	Treatment Exposures	Treatment % of Total	Relative Lift	Significant?
iOS	48.7%	1,234	45.2%	55.4%	1,456	54.8%	+13.6%	Yes (p=0.02)
Android	63.9%	567	20.8%	65.1%	589	22.2%	+1.9%	No (p=0.45)
Web	51.2%	928	34.0%	50.8%	611	23.0%	-0.8%	No (p=0.89)

Calculate % of Total:

• Sum all exposures across segments to get total
• Show each segment's share: (segment exposures / total exposures) × 100%
• This reveals which segments drive overall results

Key insights:

• Identify segments where treatment performs best (targeted rollout opportunity)
• Identify segments where treatment hurts (consider exclusions)
• Explain why different segments show different performance
• Watch for Simpson's Paradox: Overall result may differ from all segment results

Use groupByLimit: 10 to avoid overwhelming output.

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Step 6: Assess Duration & Runtime Sufficiency

Duration Assessment: Based on the power and precision analysis from Step 2, evaluate if the experiment has run long enough:

Runtime factors:

• Minimum duration: Has experiment run at least 1-2 weeks to capture full user lifecycle?
• Learning effects: For feature changes, have users had time to adapt? (typically 3-7 days)
• Weekly seasonality: Has experiment captured at least one complete week to account for day-of-week patterns?
• Business cycles: For B2B products, has it run through full business week patterns?

Integration with Step 2 power analysis:

• If Step 2 showed adequate power (>80%) AND p < 0.05: Experiment has sufficient data, duration is adequate
• If Step 2 showed low power (<70%) AND p > 0.05: Inconclusive due to insufficient data, extend duration
• If Step 2 showed adequate power (>80%) AND p > 0.15: Sufficient data to accept null result (no effect)
• If Step 2 showed adequate power but CI width too wide: Need more data for precision, extend duration

Velocity projection (only if extending recommended):

• Current daily enrollment: Calculate users per day per variant
• Days to target sample: Based on Step 2 power calculation, how many more days needed?
• Days to target precision: Based on Step 2 CI width calculation, how many more days to reach desired precision?
• Recommendation: Provide specific date when experiment should reach sufficient power/precision

Do NOT repeat the power calculations from Step 2 - reference those findings and focus on duration and timeline recommendations.

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Step 7: Understand Why (Qualitative Context)

For significant results (positive or negative):

Use Amplitude:get_feedback_insights:

• Filter by experiment date range
• For wins: look for ["lovedFeature", "mentionedFeature"]
• For losses: look for ["bug", "complaint", "painPoint"]
• Check if themes align with experiment hypothesis

Connect quantitative to qualitative:

• Explain the lift with user quotes or feedback themes
• Present 2-3 representative examples with specific details

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Step 8: Synthesize Findings and Make Recommendation

Before finalizing, verify you have included:

• ✓ All primary metric data (lift, CI, p-value, interpretation)
• ✓ All data quality findings (SRM, sample size, power, precision, all 7 validity flags with actual values)
• ✓ All secondary metrics and guardrails (with actual values and significance)
• ✓ All segment analysis tables (formatted with % of total exposures)
• ✓ Statistical power assessment (current power, required sample, duration)
• ✓ Qualitative insights (feedback themes)

Present structured analysis:

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Experiment Analysis: [Experiment Name]

Overview:

• Hypothesis: [What was tested and expected impact]
• Duration: [Start] to [End] ([X days])
• Sample Size: Control: [N] | Treatment: [N]
• Link: [Experiment URL]

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Data Quality Assessment:

Traffic & SRM:

• Traffic Balance: Control [X%] | Treatment [Y%] (Expected: [X%] | [Y%])
• SRM Detected: [Yes/No] [If yes, explain deviation severity]

Sample Size & Power:

• Sample Size: Control: [N] | Treatment: [N]
• Sample Adequacy: [Adequate (>1000) / Moderate (100-1000) / Low (<100)]
• Statistical Power: [X%] to detect [Y%] lift (Target: 80%+)
• Achieved Precision: 95% CI width = [±X%] ([High <5% / Moderate 5-10% / Low >10%])

Statistical Validity Flags: [Only include flags that failed - if all pass, state "All statistical validity checks passed"]

• ❌ statsAssumptionsMetForWholeExperiment: Statistical assumptions not met - [brief impact]
• ❌ hasSuspiciousUplift: Unusually large effect detected - [brief recommendation]
• ❌ isVariancePositive: Invalid variance - [critical issue description]
• ❌ isConfidenceIntervalNotFlipped: CI calculation error - [critical issue description]
• ❌ isStandardErrorLargeEnough: Insufficient standard error - [impact on precision]
• ❌ isPointEstimateInsideConfidenceInterval: Statistical inconsistency - [calculation issue]
• ❌ isMeanValid: Invalid mean value - [data quality issue]

Duration:

• Runtime: [X days] (Started: [date])
• Sufficiency: [Adequate - captured full user lifecycle / Need more time - [reason]]
• Recommendation: [Continue running for X more days / Sufficient data to conclude]

Overall Data Quality: [Excellent / Good / Concerns / Critical Issues] [One sentence summary of whether results can be trusted]

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Primary Metric: [Metric Name]

Variant	Value	Lift	95% CI	P-value	Status
Control	[X]	—	—	—	—
Treatment	[Y]	[+Z%]	[[A, B]]	[P]	✅ Significant

Interpretation: [1-2 sentences on statistical AND practical significance]

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Secondary Metrics & Guardrails:

Guardrails:

• ✅ Revenue per user: No regression ([+X%], p=[P])
• ✅ Retention D7: Slight positive ([+X%], p=[P])
• 🚩 Bounce rate: Regression detected ([+X%], p=[P]) ⚠️

Secondary Metrics:

• [Metric]: [+X% lift, p=[P]] - [brief interpretation]
• [Metric]: No significant change (p=[P])

Unintended Consequences: [List any negative impacts on secondary metrics or guardrails]

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Segment Analysis:

By Platform:

Segment	Control Rate	Control Exp	Control %	Treatment Rate	Treatment Exp	Treatment %	Lift	Sig?
[Data from query_experiment with groupBy]

Key Finding: [Which segments drove results; which showed differential effects]

By User Tenure: [Similar table]

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Statistical Power:

• Current Power: [X%] - [Adequate/Underpowered]
• Required Sample: Need [X] more users per variant for 80% power
• Estimated Duration: [X] more days at current traffic to reach significance

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Why This Result:

• [Feedback theme] ([X mentions])
  • "[Quote]" - [Source] ([Date])

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Recommendation: ✅ SHIP / ⚠️ ITERATE / ❌ ABANDON / 🔄 NEED MORE DATA

Rationale:

1. [Primary metric result with statistical and practical significance]
2. [Guardrail status and any unintended consequences]
3. [Segment insights - opportunities or concerns]
4. [Power analysis - adequate data or need more time]
5. [Qualitative validation]

Known Risks:

• [Risk 1 with mitigation if shipping]
• [Risk 2 with mitigation if shipping]

Next Steps:

1. [Specific action based on recommendation]
2. [Follow-up or monitoring action]

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Key Takeaways (3-5 actionable insights):

1. [Most important finding]
2. [Second most important finding]
3. [Third most important finding]
4. [Additional insight if relevant]

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Key Scenarios & How to Handle

Inconclusive Results (p > 0.05)

Diagnose:

• Check statistical power: Is sample size adequate? Report current power percentage
• Check confidence interval: Very wide = high variance, need more data
• Check segments: Effect may exist in specific subgroup

Action:

• If power <60%: Extend duration or increase traffic allocation
• If power >80% but p >0.15: Accept null result (no effect detected)
• Check segment tables: Look for subgroups with significant effects

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Guardrail Regressed

Diagnose:

• Quantify trade-off with specific numbers: +10% conversion but -2% retention
• Which segments drove the regression? Check segment tables
• Is regression statistically significant or just noise?

Action:

• Small regression + large primary win + not significant = ship with monitoring
• Significant regression on critical metric = iterate to fix or abandon
• Segment-specific regression = consider targeted rollout excluding affected segments

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Segment Tables Show Opposite Effects

Simpson's Paradox detected:

• Overall result may be misleading if segments show opposite directions
• Example: Overall +5% lift, but iOS -10%, Android +15%

Action:

• Report the paradox clearly with specific segment numbers
• Consider targeted rollout to segments that benefit
• Exclude or iterate for segments that are harmed

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Best Practices

Comprehensive analysis:

• ✅ Include ALL data from tool calls with specific numbers
• ✅ Format segment analysis as breakdown tables with % of total
• ✅ Check statistical power and duration adequacy
• ✅ Verify data quality before drawing conclusions
• ✅ Connect quantitative results to qualitative insights

Statistical rigor:

• ✅ Report confidence intervals, not just p-values
• ✅ Distinguish statistical vs. practical significance
• ✅ Apply multiple testing correction for 5+ metrics
• ✅ Check for Simpson's Paradox in segment analysis

Avoid:

• ❌ Don't provide brief summaries - be comprehensive
• ❌ Don't omit data quality issues or negative secondary metrics
• ❌ Don't ignore segments - they reveal critical insights
• ❌ Don't make recommendations without adequate power
• ❌ Don't stop analysis early because primary looks good

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For Experiment Design

If user wants to design a new experiment, guide them through:

1. Define hypothesis: "We believe [change] will cause [users] to [behavior] because [reason]"

2. Select metrics:

   • Use Amplitude:search with entityTypes: ["METRIC"] to find candidates
   • Primary: directly measures hypothesis
   • Guardrails: revenue, retention, core engagement (prevent unintended consequences)

3. Estimate sample size:

   • Typical: 1-2 weeks minimum, 1000+ users per variant
   • Higher variance metrics need more data
   • Use Amplitude:query_chart to check metric's historical variance

4. Create experiment:

   • Use Amplitude:create_experiment with projectIds, variants, and metrics
   • Return experiment ID, URL, and deployment key for engineering

For detailed setup guidance, consider using the setup-experiment-and-flags skill.