You get:
- executives glazing over when you show regression tables
- decisions made on “vibes” instead of data because no one can interpret the stats
- miscommunication between analysts and decision-makers
- findings that are statistically significant but practically meaningless (and no one notices)
- confidence intervals ignored because they’re not understood
But statistical outputs tell stories:
- coefficient → “each unit of X changes Y by Z amount”
- p-value → “how likely this pattern is just random noise”
- confidence interval → “the range of plausible effects”
- R-squared → “how much of the story this model tells”
- interaction → “it depends on something else”
Without translation, data insights die in spreadsheets.
This prompt turns statistics into English.
Assume the role of a data storyteller who translates statistics for business audiences.
Your task is to convert statistical output into plain-English implications.
Generate:
1. THE HEADLINE (one sentence)
- What the most important finding means for the business
2. PLAIN-ENGLISH TRANSLATIONS (per key variable)
- Variable name
- Statistical finding (coefficient, p-value, CI)
- Translation: "For every [unit increase in X], we see [effect size] change in Y"
- Practical significance: "This is equivalent to [real-world example]"
3. CERTAINTY ASSESSMENT
- How confident are we? (p-value → "very confident" if p<.01, "somewhat" if p<.05, "weak" if p>.05)
- What could change this finding? (confounding variables, sample limitations)
4. WHAT THIS MEANS FOR ACTION
- Should we act? (Yes / No / Investigate further)
- What kind of action? (size, timing, scope)
5. PLAIN-ENGLISH SUMMARY (2-3 sentences for an executive)
INPUTS:
Statistical output (regression table, t-test, ANOVA, etc.):
[PASTE TABLE OR RESULTS]
Business context:
[E.G., "We're deciding whether to increase ad spend"]
Audience:
[EXECUTIVE / PRODUCT MANAGER / MARKETING TEAM]
Key question to answer:
[E.G., "Does email personalization increase conversion?"]
RULES:
- Never say "statistically significant" without explaining what it means
- Flag when a result is statistically significant but practically tiny
- Use analogies and comparisons ("equivalent to X months of growth")
- Distinguish between correlation and causation explicitly
- Run this before any presentation with non-technical stakeholders.
- Use the “headline” as your slide title — it should make sense to anyone.
- For each variable in your regression, generate a translation — then cut the unimportant ones.
- Flag practical insignificance early — it saves your team from chasing small effects.
- Send the “executive summary” version as a pre-read before meetings.
Statistical output:
“Regression: Email open rate → conversion. Coefficient = 0.023, p = 0.008, 95% CI [0.007, 0.039]. R-squared = 0.12. Control variables: campaign type, day of week, customer segment.”
Business context:
“We’re deciding whether to invest in email subject line optimization”
Audience:
VP of Marketing
Key question to answer:
“Does improving email open rates actually increase sales?”
This framework improves outcomes by forcing:
- headline distillation (one clear takeaway)
- plain-English translation (no jargon allowed)
- practical significance check (is this worth acting on?)
- certainty communication (honest about what we don’t know)
- action implication (what to do next)
Great statistical translation doesn’t dumb down — it makes insight accessible.
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