---
name: statistical-results-plain-language-translator
description: "Translates statistical outputs — p-values, confidence intervals, regression coefficients, effect sizes, and model metrics — into clear business language for PMs, finance partners, and executives, with a headline takeaway, plain-language explanation, confidence framing, and recommended action for each result. Use when preparing to present analysis results, A/B test outcomes, or model performance to non-technical stakeholders who need to make decisions."
argument-hint: "[statistical-results] [audience]"
allowed-tools: ["Read"]
---

# Statistical Results Plain-Language Translator

## Purpose
Convert raw statistical outputs into decision-ready business language — so stakeholders understand what the numbers mean, how confident they should be, and what to do next, without misinterpreting significance as importance or ignoring uncertainty. Eliminates the gap between a correct analysis and a decision the analysis should actually drive.

## When to Use
Invoke this skill when the user asks to:
- Explain A/B test results to a PM or growth team
- Translate a regression or model output into business terms for executives
- Prepare a readout of experiment results for a decision meeting
- Communicate confidence intervals or p-values to finance or leadership
- Rewrite a technical analysis summary so non-technical stakeholders can act on it
- Frame statistical uncertainty without either overclaiming or burying the finding

## Inputs Required
Before starting, collect:
- **Statistical results**: The raw outputs — p-values, confidence intervals, coefficients, effect sizes, lift percentages, sample sizes, model metrics (AUC, R², RMSE, etc.)
- **What was tested/modeled**: The business question or hypothesis behind the analysis
- **Audience**: PM / growth / finance / executive / mixed — and their comfort level with data
- **Decision at stake**: What choice this analysis is meant to inform
- **Business context**: Relevant baselines, revenue figures, or KPIs the result affects
- **Practical significance threshold**: The minimum effect size that would actually matter to the business (if known)
- **Known caveats**: Any data quality issues, confounders, or scope limits the analyst is aware of

If the practical significance threshold is unknown, infer a reasonable one from the business context and state the assumption explicitly.

## Instructions

### Step 1: Classify Each Result
For every statistical output provided, tag it as one of:
- **Statistically significant AND practically meaningful** — real and worth acting on
- **Statistically significant but practically trivial** — real but too small to matter (common with large samples)
- **Not statistically significant, inconclusive** — can't distinguish from zero given the data
- **Not statistically significant, but confidently null** — narrow interval around zero; genuine "no effect" finding
- **Directionally suggestive but underpowered** — points somewhere, but sample too small to trust

This classification drives the framing. Never let statistical significance stand in for business importance — separate the two explicitly.

### Step 2: Translate the Core Concepts
Apply these standard translations, calibrated to the audience:

**P-value** → likelihood-of-noise framing, not the technical definition.
- Say: "If there were truly no effect, we'd see a result this strong only about X% of the time — so we're fairly confident this is real."
- Never say: "There is an X% chance the null hypothesis is true" (this is wrong and will be repeated by stakeholders).

**Confidence interval** → plausible-range framing.
- Say: "Our best estimate is a 4.2% lift, and the true effect is very likely somewhere between 1.1% and 7.3%."
- Anchor decisions on the interval's edges: "Even in the pessimistic case, we'd still gain at least 1.1%."

**Regression coefficient** → holding-others-constant, per-unit-change framing.
- Say: "Each additional onboarding email is associated with roughly $12 more in first-month spend, all else equal."
- Flag correlation vs. causation explicitly when the design is observational.

**Effect size / lift** → business-magnitude framing.
- Convert to revenue, users, hours, or the KPI the audience owns. "A 3% conversion lift ≈ ~1,800 additional sign-ups per month at current traffic."

**Model metrics** → what-it-means-for-decisions framing.
- AUC/accuracy: "The model correctly ranks a random converter above a random non-converter about 82% of the time — good enough to prioritize outreach, not to auto-approve."
- Frame every metric against how the model will actually be used.

### Step 3: Build a Translation Block for Each Result
For each finding, produce a four-part block:
1. **Headline takeaway** (one sentence, plain language, leads with the business meaning)
2. **What this means** (2–3 sentences unpacking the result in the audience's terms)
3. **How confident we should be** (uncertainty framed honestly — interval edges, sample caveats, or "this needs more data")
4. **Recommended action** (what to do given the result AND its confidence level)

### Step 4: Write the Executive Summary
Open with a 3–4 sentence summary that answers, in order: What did we find? Can we trust it? What should we do? Lead with the decision, not the methodology.

### Step 5: Surface the Caveats Section
List limitations plainly — sample size, time window, confounders, external validity. Frame each caveat by its decision impact ("this means we shouldn't roll out to new markets yet"), not as academic hedging.

## Output Format
- **Structure**: Executive summary → one translation block per result → caveats → recommended next steps
- **Length**: 600–1,000 words depending on number of results
- **Format**: Prose executive summary; each result as a labeled four-part block (Headline / What this means / Confidence / Action); caveats as a short bulleted list
- **Sections**: Executive Summary → Findings (translation blocks) → Caveats & Limitations → Recommended Next Steps
- **Tone**: Trusted analyst briefing a decision-maker — plain, direct, honest about uncertainty, no jargon left untranslated, no false precision

## Quality Checklist
Before delivering output, verify:
- [ ] Every statistical result provided has its own four-part translation block
- [ ] No p-value is described as "the probability the null is true" or "the probability the result is due to chance" (both are wrong)
- [ ] Statistical significance and practical significance are addressed separately for each finding
- [ ] At least one effect size is converted into a concrete business unit (revenue, users, or the audience's KPI)
- [ ] Every finding includes an explicit confidence framing — no result is presented as certain
- [ ] Each recommended action matches the confidence level (no "roll it out" on an inconclusive result)

## Notes
- The most common failure is stakeholders hearing "significant" and assuming "big" — the practical-vs-statistical split in Step 1 exists to prevent exactly this.
- With very large samples, nearly everything is statistically significant; lean harder on effect size and the practical threshold.
- With small samples, resist the urge to soften a null into a positive — "we couldn't detect an effect" is a legitimate, useful finding.
- When the design is observational, say "associated with" not "causes" every time — one careless "causes" can trigger a bad decision.
- If results conflict (e.g., significant p-value but interval crossing the practical threshold), name the tension directly rather than picking the flattering interpretation.

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## How to use this skill

This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top.

**Installing in Claude Code:**
- `~/.claude/skills/<folder-name>/SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
- `<your-project>/.claude/skills/<folder-name>/SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

**Using in Claude.ai, ChatGPT, Gemini, or other AI tools:**

1. Drag this file into a new chat, or copy and paste its contents
2. Send your task or request as your next message — the AI will apply the skill where it fits

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