analyze

Statistical & Business Analysis

Frameworks for answering business questions with data: descriptive statistics, hypothesis testing, cohort analysis, segmentation, trend detection, KPI calculation, and pre-delivery QA.

Scripts

Script	Usage
descriptive_stats.py	uv run ${CLAUDE_SKILL_DIR}/scripts/descriptive_stats.py data.csv --group segment --value revenue
hypothesis_test.py	uv run ${CLAUDE_SKILL_DIR}/scripts/hypothesis_test.py data.csv --col value --group segment --a control --b treatment
ab_test.py	uv run ${CLAUDE_SKILL_DIR}/scripts/ab_test.py data.csv --col converted --group variant --control A --treatment B
cohort_analysis.py	uv run ${CLAUDE_SKILL_DIR}/scripts/cohort_analysis.py data.csv --user user_id --date order_date
rfm_segmentation.py	uv run ${CLAUDE_SKILL_DIR}/scripts/rfm_segmentation.py data.csv --customer customer_id --date order_date --value revenue
trend_analysis.py	uv run ${CLAUDE_SKILL_DIR}/scripts/trend_analysis.py data.csv --date date --value revenue --window 30
validate.py	uv run ${CLAUDE_SKILL_DIR}/scripts/validate.py data.csv
chart_templates.py	uv run ${CLAUDE_SKILL_DIR}/scripts/chart_templates.py data.csv --type bar --x category --y value -o chart.png

Analysis type selection

Question	Analysis type	Script
What happened?	Descriptive statistics, aggregations	descriptive_stats.py
Why did it happen?	Diagnostic analysis, drill-downs, segmentation	rfm_segmentation.py
Is this difference real?	Hypothesis testing (t-test, chi-square)	hypothesis_test.py
Did the change work?	A/B test analysis	ab_test.py
How do groups behave over time?	Cohort analysis	cohort_analysis.py
What are the natural groupings?	Segmentation / clustering	rfm_segmentation.py
What are the trends?	Time series decomposition, rolling averages	trend_analysis.py
What should we track?	KPI definition and dashboarding	descriptive_stats.py
Is this ready to share?	Pre-delivery QA, sanity checking	validate.py

Choosing the right measure of center

Situation	Use	Why
Symmetric distribution, no outliers	Mean	Most efficient estimator
Skewed distribution (revenue, duration)	Median	Robust to outliers
Categorical or ordinal data	Mode	Only option for non-numeric
Highly skewed with outliers	Median + mean	The gap shows skew

Always report mean and median together for business metrics. If they diverge significantly, the data is skewed and the mean alone is misleading.

Choosing the right test

Scenario	Test
Compare 2 group means (normal)	Independent t-test
Compare 2 group means (non-normal)	Mann-Whitney U
Compare 2 paired measurements	Paired t-test
Compare 3+ group means	One-way ANOVA
Compare proportions	Chi-square test
Test correlation	Pearson / Spearman
Test normality	Shapiro-Wilk

The hypothesis_test.py script auto-selects the right test based on normality checks and reports p-value, effect size (Cohen's d), and confidence interval.

Effect size interpretation

Cohen's d	Interpretation
< 0.2	Negligible
0.2 - 0.5	Small
0.5 - 0.8	Medium
> 0.8	Large

KPI framework

Category	KPI	Formula
Revenue	MRR	Sum of monthly recurring revenue
Revenue	ARPU	Total revenue / active users
Growth	MoM Growth	(this_month - last_month) / last_month
Retention	Churn Rate	Lost customers / start customers
Retention	Retention Rate	1 - churn rate
Engagement	DAU/MAU	Daily active / monthly active
Efficiency	CAC	Marketing spend / new customers
Efficiency	LTV	ARPU * avg lifetime months
Efficiency	LTV:CAC	LTV / CAC (target: > 3:1)
Conversion	Conversion Rate	Conversions / visitors
Conversion	Funnel Drop-off	Lost at each stage / entered stage

Analysis report format

=== Analysis Report ===
Question: [What business question are we answering?]
Data: [Dataset, date range, filters applied]
Method: [Statistical test / analysis type used]

Key Findings:
1. [Most important finding with numbers]
2. [Second finding]
3. [Third finding]

Statistical Evidence:
- Test: [name], p-value: [value], effect size: [value]
- Confidence interval: [range]

Caveats:
- [Sample size limitations]
- [Selection bias concerns]
- [Missing data impact]

Recommendation:
[Actionable next step based on findings]

Simple forecasting (for analysts, not data scientists)

Method	How	When
Naive	Tomorrow = today	Baseline
Seasonal naive	Tomorrow = same day last week/year	Seasonal data
Linear trend	Fit a line to historical data	Clearly linear trends
Moving average	Trailing average as forecast	Noisy data

Always communicate uncertainty — provide a range, not a point estimate:

• "We expect 10K-12K signups next month based on the 3-month trend"
• NOT "We will get exactly 11,234 signups next month"

When to escalate to a data scientist: Non-linear trends, multiple seasonalities, external factors, or when forecast accuracy matters for resource allocation.

Statistical pitfalls to watch for

Simpson's Paradox

A trend in aggregated data can reverse when segmented. Always check whether conclusions hold across key segments.

Multiple Comparisons Problem

Testing 20 metrics at p=0.05 means ~1 will be falsely significant. Apply Bonferroni correction (alpha / number of tests) or report how many tests were run.

Ecological Fallacy

Aggregate trends may not apply to individuals. "Countries with higher X have higher Y" does NOT mean individuals with higher X have higher Y.

Anchoring on False Precision

• "Churn will be 4.73% next quarter" implies more certainty than warranted
• Prefer ranges: "We expect churn between 4-6%"

Correlation vs Causation

When you find a correlation, consider:

• Reverse causation: Maybe B causes A, not A causes B
• Confounding: Maybe C causes both A and B
• Coincidence: With enough variables, spurious correlations are inevitable

What you can say: "Users who use feature X have 30% higher retention" What you cannot say: "Feature X causes 30% higher retention"

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Visualization

Quick chart generation

uv run ${CLAUDE_SKILL_DIR}/scripts/chart_templates.py data.csv --type bar --x category --y value -o chart.png
uv run ${CLAUDE_SKILL_DIR}/scripts/chart_templates.py data.csv --type line --x date --y value --hue segment -o trend.png
uv run ${CLAUDE_SKILL_DIR}/scripts/chart_templates.py data.csv --type hist --x value -o dist.png
uv run ${CLAUDE_SKILL_DIR}/scripts/chart_templates.py data.csv --type heatmap -o correlations.png
uv run ${CLAUDE_SKILL_DIR}/scripts/chart_templates.py data.csv --type scatter --x feature_a --y target -o scatter.png
uv run ${CLAUDE_SKILL_DIR}/scripts/chart_templates.py data.csv --type box --x group --y value -o box.png

Chart selection guide

Question	Chart type
How does X change over time?	Line chart
How do categories compare?	Bar chart (horizontal if many categories)
What is the distribution?	Histogram, box plot, violin plot
How do two variables relate?	Scatter plot
What are the correlations?	Heatmap
What is the composition?	Stacked bar
How do groups differ?	Grouped bar, box plot by group
What are the top/bottom N?	Horizontal bar, sorted
Multi-dimensional?	Pair plot

Framework selection

Framework	Best for	Output
matplotlib	Static charts, publications, fine control	PNG, PDF, SVG
seaborn	Statistical plots, quick EDA visuals	PNG, PDF, SVG
plotly	Interactive charts, dashboards, web	HTML, JSON
altair	Declarative, concise, notebooks	HTML, JSON

Default: matplotlib + seaborn. Interactive: plotly (self-contained HTML).

Design principles

• Title every chart — no unnamed plots
• Label axes with units (e.g., "Revenue ($M)", not "revenue")
• Bar charts start at zero — always
• Sort bars descending by value unless there's a natural order
• Limit categories — max 7–8 colors; group rest as "Other"
• Use colorblind-safe palettes: sns.color_palette("colorblind")
• Avoid pie charts (>5 categories), 3D charts (never), dual-axis (use cautiously)
• Save to figures/ directory; use descriptive filenames (revenue_by_quarter.png)
• Always plt.close() after saving to avoid memory leaks

See references/chart-selection.md for the full chart reference.

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Rules

• State the business question BEFORE running any analysis
• Always check sample sizes — small samples produce unreliable results
• Report effect sizes alongside p-values — statistical significance is not practical significance
• Use confidence intervals, not just point estimates
• Segment before aggregating — averages hide important patterns
• Check for confounding variables before claiming causation
• Round results appropriately — false precision erodes trust
• Explain findings in plain language — stakeholders don't read code

Pre-Delivery QA & Validation

Pre-delivery QA checklist, common data analysis pitfalls, result sanity checking, and documentation standards.

Pre-Delivery QA Checklist

Run through before sharing any analysis with stakeholders.

Data Quality Checks

• Source verification: Confirmed which tables/data sources were used. Are they the right ones?
• Freshness: Data is current enough. Noted the "as of" date.
• Completeness: No unexpected gaps in time series or missing segments.
• Null handling: Checked null rates in key columns. Nulls handled appropriately (excluded, imputed, or flagged).
• Deduplication: No double-counting from bad joins or duplicate source records.
• Filter verification: All filters are correct. No unintended exclusions.

Calculation Checks

• Aggregation logic: GROUP BY includes all non-aggregated columns. Aggregation level matches analysis grain.
• Denominator correctness: Rate and percentage calculations use the right denominator. Denominators are non-zero.
• Date alignment: Comparisons use same time period length. Partial periods excluded or noted.
• Join correctness: JOIN types appropriate. Many-to-many joins haven't inflated counts.
• Metric definitions: Metrics match how stakeholders define them. Deviations noted.
• Subtotals sum: Parts add up to the whole where expected. If not, explained why.

Reasonableness Checks

• Magnitude: Numbers in plausible range. No negative revenue. Percentages between 0-100%.
• Trend continuity: No unexplained jumps or drops in time series.
• Cross-reference: Key numbers match other known sources (dashboards, prior reports).
• Order of magnitude: Totals in the right ballpark vs known figures.
• Edge cases: Checked boundaries — empty segments, zero-activity periods, new entities.

Presentation Checks

• Chart accuracy: Bar charts start at zero. Axes labeled. Scales consistent across panels.
• Number formatting: Appropriate precision. Consistent formatting. Thousands separators.
• Title clarity: Titles state the insight, not just the metric. Date ranges specified.
• Caveat transparency: Known limitations and assumptions stated explicitly.
• Reproducibility: Someone else could recreate this analysis from the documentation.

Common Data Analysis Pitfalls

Join Explosion

A many-to-many join silently multiplies rows, inflating counts and sums. Always check row counts after joins. Use COUNT(DISTINCT id) instead of COUNT(*) when counting entities through joins.

Survivorship Bias

Analyzing only entities that exist today, ignoring those that churned, failed, or were deleted. Ask "who is NOT in this dataset?" before drawing conclusions.

Incomplete Period Comparison

Comparing a partial period to a full period. "January revenue is $500K vs December's $800K" — but January isn't over yet. Filter to complete periods, or compare same-number-of-days.

Denominator Shifting

The denominator changes between periods, making rates incomparable. Use consistent definitions across all compared periods. Document any changes.

Average of Averages

Averaging pre-computed averages gives wrong results when group sizes differ. Always aggregate from raw data. Never average pre-aggregated averages.

Timezone Mismatches

Different data sources use different timezones, causing misalignment. Standardize all timestamps to a single timezone (UTC recommended) before analysis.

Selection Bias in Segmentation

Segments defined by the outcome you're measuring, creating circular logic. Define segments based on pre-treatment characteristics, not outcomes.

Result Sanity Checking

Magnitude Checks

Metric Type	Sanity Check
User counts	Match known MAU/DAU figures?
Revenue	Right order of magnitude vs known totals?
Rates	Between 0% and 100%? Match dashboard?
Growth rates	Is 50%+ MoM realistic or a data issue?
Averages	Reasonable given the distribution?
Percentages	Segment percentages sum to ~100%?

Cross-Validation Techniques

1. Calculate the same metric two different ways and verify they match
2. Spot-check individual records — pick specific entities and trace manually
3. Compare to known benchmarks — match against dashboards, prior reports
4. Reverse engineer — if total revenue is X, does per-user revenue times user count equal X?
5. Boundary checks — filter to a single day/user/category. Are micro-results sensible?

Red Flags That Warrant Investigation

• Any metric changed >50% period-over-period without obvious cause
• Counts or sums that are exact round numbers (filter or default value issue)
• Rates exactly at 0% or 100% (incomplete data)
• Results that perfectly confirm the hypothesis (reality is messier)
• Identical values across time periods or segments (query ignoring a dimension)

Documentation Template

Every non-trivial analysis should include:

## Analysis: [Title]

### Question
[The specific question being answered]

### Data Sources
- Table/file: [name] (as of [date])

### Definitions
- [Metric A]: [How it's calculated]
- [Segment X]: [How membership is determined]
- [Time period]: [Start] to [end], [timezone]

### Methodology
1. [Step 1]
2. [Step 2]

### Assumptions and Limitations
- [Assumption and why it's reasonable]
- [Limitation and its impact on conclusions]

### Key Findings
1. [Finding with evidence]

### Caveats
- [Things the reader should know before acting on this]

Validation Rules

• Run the full checklist before sharing — skipping steps is how errors ship
• Check row counts before and after every join
• Never average pre-aggregated averages — go back to raw data
• Always ask "who is missing from this dataset?"
• Cross-reference key numbers against at least one other source
• State assumptions explicitly — unstated assumptions become invisible errors
• Round appropriately — false precision erodes trust