Statistical results plain-language translator · free AI skill

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…

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:

Headline takeaway (one sentence, plain language, leads with the business meaning)
What this means (2–3 sentences unpacking the result in the audience's terms)
How confident we should be (uncertainty framed honestly — interval edges, sample caveats, or "this needs more data")
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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