[Fiona Freelance Financier]

Hi — I’m a non-technical user curious how to get helpful explanations from an AI when I have statistical results (like t-tests, regressions, or charts). I want a short, clear summary I can share with colleagues who aren’t statisticians.

What is the best prompt to ask an AI so it:

• Explains the main findings in plain language, avoiding jargon,
• Highlights practical meaning (effect size and direction),
• Notes important assumptions or caveats in simple terms, and
• Gives a one-sentence summary and a suggested one- or two-line explanation for a presentation slide.

If helpful, include a short example prompt I could copy and paste, and a brief example of the kind of answer I should expect. I’m looking for clear templates I can reuse.

Thanks — any sample prompts or tips for phrasing would be very welcome!

[Jeff Bullas]

Good question — wanting statistics explained in plain language is exactly the right place to start. A quick win: paste the short prompt below into an AI tool and you’ll get a clear, non-technical explanation in under a minute.

What you’ll need (under 5 minutes):

• A short summary of the results: test name (e.g., t-test, chi-square), key numbers (p-value, effect size, confidence interval), sample size.
• A one-sentence description of the audience (e.g., senior managers, customers, patients).

Copy-paste prompt (quick win):

“Explain these statistical results in plain language for a non-technical audience: test = two-sample t-test; mean group A = 5.2, mean group B = 2.9; difference = 2.3; p = 0.03; 95% CI = (0.2, 4.4); n = 50. Say what this means for decision-makers, how confident we should be, and one short suggestion for next steps.”

Step-by-step: how to do it and what to expect

1. Paste the prompt above into your AI chat or assistant.
2. Replace the numbers with your own results and change the audience line if needed.
3. Read the AI’s output. Expect: a short interpretation, a plain-language meaning, confidence level, and a recommended next step.
4. If it’s too technical, ask: “Make that even simpler — 3 bullet points for busy readers.”

Example (what you’ll get):

Plain language: “Group A’s average is 2.3 points higher than Group B’s, and the p-value of 0.03 suggests this difference is unlikely due to random chance. The 95% confidence interval (0.2 to 4.4) means the true difference is likely between a small and moderate positive effect. For decision-makers: the result supports choosing the approach in Group A, but consider running a larger follow-up test to confirm. Next step: pilot the change with a bigger sample or track outcomes over time.”

Common mistakes & fixes

• Mistake: Vague prompt. Fix: Give specific numbers and audience.
• Mistake: Asking only for technical jargon. Fix: Ask explicitly for “plain language” and “1-sentence summary”.
• Mistake: No context on implications. Fix: Add “What should a decision-maker do next?” to the prompt.

Short action plan (do this now):

1. Gather your key numbers and audience description (5 minutes).
2. Use the copy-paste prompt and review the explanation (5 minutes).
3. Refine by asking for a one-line recommendation and a single risk to watch (5 minutes).

Reminder: The goal is useful decisions, not perfect statistics. Use AI to translate numbers into clear actions and always note uncertainty. Try the prompt now and tweak it until it fits your audience.

[Steve Side Hustler]

Nice — you’re on the right track. Here’s a compact, practical way to get clear, non-technical explanations from an AI so you can share results with busy people without drowning them in stats-speak.

What you’ll need (5 minutes):

• Key numbers: test type (e.g., t-test, chi-square), p-value, effect size or difference, confidence interval, and sample size.
• One-line audience description (e.g., finance director, front-line staff, clients).
• A short goal: decision, explanation, or next step you want from the reader.

How to ask — a simple recipe (don’t copy-paste; adapt this):

• Start: say you want a plain-language explanation for a specific audience.
• Include: the test name and the exact numbers (p, effect, CI, n).
• Ask for three outputs: a one-sentence summary, a confidence sentence (how sure to be), and one practical recommendation.

Variant prompts to try (short phrases to mix in):

• Short: “Give me a one-line takeaway and three bullets for busy readers.”
• Manager-friendly: “Say what this means for budgeting or policy decisions.”
• Risk-aware: “Add one sentence about a key limitation or what to watch next.”
• Next-step focused: “Suggest a single, low-cost follow-up (pilot, extra data, monitoring).”

Step-by-step workflow (10–15 minutes total):

1. Gather your numbers and one-line audience goal (5 minutes).
2. Use the recipe above to craft a short request in your AI tool (2 minutes).
3. Read the AI’s reply. Expect: one-sentence takeaway, plain-language meaning, confidence note, and one action recommendation (2–5 minutes).
4. Refine once: ask for simpler wording, a 3-bullet summary, or a short caveat if needed (1–2 minutes).

What to expect: a readable 4–6 line explanation you can paste into an email or slide. It won’t replace a statistician’s report, but it will turn numbers into an action-oriented sentence and a single practical next step.

Quick tip: if the AI leans too technical, ask: “Rewrite that for someone who skips the details — 3 bullets, no jargon.” That usually gets your audience-friendly result fast.

[Rick Retirement Planner]

Nice, that recipe is exactly the right foundation: short inputs, clear audience, and three concrete outputs make it easy to turn numbers into action. I especially like calling out a single goal — that helps the AI focus on whether you want a decision, an explanation, or a next step.

One thing people often trip over is the p-value. In plain English: a p-value tells you how surprising the observed difference would be if there really were no effect. If the p-value is small, the data are unlikely under the “no effect” story, so we lean toward thinking there is a real difference. It does not say how big the effect is, nor does it prove the result is practically important — it just flags that the result isn’t easily explained by random chance alone.

1. What you’ll need (5 minutes):
   • Test type and the key numbers: p-value, effect size or difference, confidence interval, and sample size.
   • A one-line description of the audience and the decision you want them to make.
   • One short constraint: e.g., “keep to three bullets” or “single recommendation for a pilot.”
2. How to ask (2–5 minutes):
   • Tell the AI you want plain language for your named audience.
   • Give the numbers and ask for three things: a one-sentence takeaway, a single sentence about confidence (how sure to be), and one practical next step or caveat.
   • Use short trigger phrases rather than a long prompt — for example: “plain language,” “one-sentence takeaway,” “confidence note,” “one recommended next step.”
3. What to expect (read and adapt, 2–5 minutes):
   • A simple one-line headline you can paste into an email or slide.
   • A short plain-English explanation of what the p-value and confidence interval mean for your situation.
   • A practical recommendation (pilot, monitor, collect more data) and one caveat to watch.
4. Quick refinement (1–2 minutes):
   • If it’s still too technical, ask: “Make that 3 bullets, no jargon, for a busy manager.”
   • If you need more rigor, ask the AI to show the numbers that support the sentence (e.g., effect size and CI) in one short line.

Clarity builds confidence: keep the request focused, give the exact numbers and audience, and ask explicitly for one takeaway, one confidence sentence, and one action. That structure turns statistics into decisions without hiding the uncertainty.

[Jeff Bullas]

Quick win: you’ve nailed the structure — now use a sharp, copy-paste prompt and a short refinement loop to turn stats into a one-line decision and a single next step.

Why this matters

Busy people don’t want formulas — they want a clear takeaway, how sure to be, and one practical action. The right prompt gives you that in seconds and keeps uncertainty visible.

What you’ll need (5 minutes)

• Test type (e.g., two-sample t-test, chi-square).
• Key numbers: effect size or difference, p-value, 95% confidence interval, sample size.
• One-line audience (e.g., CFO, operations manager, clients) and your goal (decision, explanation, or next step).
• Optional constraint: length or tone (e.g., “3 bullets” or “one-sentence summary”).

Step-by-step: how to do it

1. Copy the robust prompt below and paste it into your AI tool.
2. Replace the example numbers and audience with your own.
3. Read the reply. Expect: a one-sentence takeaway, a confidence note, and one recommended next step.
4. Refine with a short follow-up like: “Make that 3 bullets, no jargon, for a busy director.”

Robust copy-paste prompt (use this)

“Explain these statistical results in plain language for a non-technical audience: test = two-sample t-test; mean group A = 5.2, mean group B = 2.9; difference = 2.3; p = 0.03; 95% CI = (0.2, 4.4); n = 50. Give: 1) one-sentence takeaway for decision-makers, 2) one short sentence about how confident we should be (mention p-value and CI in plain terms), and 3) one practical next step (low-cost). Keep it under 3 short bullets and avoid technical jargon.”

Variants to try (copy-paste and swap numbers)

• Manager-friendly: “Make that one-line takeaway show the budget or policy implication in plain English.”
• Risk-aware: “Also include one sentence on the main limitation and what to monitor next.”
• Email-ready: “Rewrite as a 2-line email summary plus one suggested subject line.”

Example output you should expect

Plain language: “Group A’s average is 2.3 points higher than Group B’s. The p-value of 0.03 means this difference is unlikely to be due to chance, and the 95% CI (0.2 to 4.4) suggests the true effect is small to moderate. Recommendation: pilot the Group A approach in one region and track outcomes over the next quarter.”

Common mistakes & fixes

• Mistake: Vague prompt. Fix: Include exact numbers and audience.
• Mistake: Asking for too much detail. Fix: Ask explicitly for a one-sentence takeaway and one action.
• Mistake: Ignoring limitations. Fix: Add “one sentence about limitations or what to watch” to the prompt.

Short action plan (do this now)

1. Collect your numbers and audience (5 minutes).
2. Use the robust prompt above and read the result (2–3 minutes).
3. Refine with a single follow-up: “Shorten to 3 bullets, no jargon” (1 minute).

Reminder: Aim for useful decisions, not perfect statistics. Use the AI to translate numbers into clear actions and always note the uncertainty you’d want a manager to know.

[aaron]

Agreed: your refinement loop is spot on — one-line takeaway, confidence, next step. Let’s turn that into a repeatable system that produces decision-ready “stat cards” every time, with measurable outcomes.

The problem

Plain-English summaries still drift into jargon, skip the business impact, or overstate shaky results. Decision-makers need consistency, thresholds, and a standard format they can trust in under a minute.

Why it matters

Clarity drives faster decisions and fewer rework cycles. A consistent output lets you compare studies, set action thresholds, and move budget with confidence.

What I’ve learned running exec comms

Two additions change the game: enforce a decision threshold (“act if…”) and make the AI red-team its own summary (two ways it could be wrong). That stops overclaiming and aligns stats with business reality.

What you’ll need (5 minutes)

• Test type, effect/difference with units, p-value, 95% CI, sample size, baseline rate if relevant.
• Audience and goal (e.g., CFO, approve pilot vs. hold).
• Your provisional decision threshold (e.g., “act if uplift ≥ 2 points and CI excludes zero”).

Copy-paste prompt: Decision-Ready Stat Card

“Act as a plain-language statistics explainer for business leaders. Use only the numbers I provide; if something essential is missing, ask one clarifying question before answering. Based on the results, produce:
1) One-sentence decision takeaway (<=25 words) framed as ‘Do X because Y.’
2) Confidence sentence in everyday terms referencing the p-value and CI.
3) One practical, low-cost next step.
4) One key limitation or risk to watch.
5) Impact framing: budget/policy/operations implication with absolute numbers and units (avoid jargon).
6) Decision threshold: state ‘Act if [effect] ≥ [my threshold] and CI excludes zero’; if CI includes zero, label as ‘suggestive, not decisive.’
7) Two versions: a) Exec email (2 lines + subject), b) Frontline 3 bullets, no jargon.
8) Self-check: two ways this could be wrong and what extra data would change the decision.
Results: test = [name]; effect/difference = [value + units]; p = [value]; 95% CI = ([low], [high]); n = [size]; baseline/rate = [if any]; audience = [who]; goal = [decision]. My threshold: [define].”

Insider trick

Force absolute numbers every time (e.g., “per 100 customers”) to stop percentage inflation. If the AI uses only relative terms, reply: “Convert to absolute counts per 100 and restate the decision in 20 words.”

Step-by-step (10–15 minutes)

1. Assemble your numbers and set a threshold (e.g., “≥ 2-point uplift; CI excludes 0”).
2. Paste the Stat Card prompt, fill the brackets, and run it.
3. Quick scan: confirm the takeaway is an action (“Do/Don’t”), the CI is interpreted plainly, and absolute numbers are present.
4. Refine once: “Shorten to 3 bullets, no jargon, include one risk and one $/time implication.”
5. Ship it: paste the exec version into your email or slide. Archive the frontline version for teams.

What to expect

• A consistent one-minute read with a clear “go/no-go/pilot” and a single, low-cost next step.
• Transparent uncertainty: if CI includes zero, you’ll see “suggestive, not decisive.”
• Two audiences covered without extra work (exec + frontline).

Follow-up prompts (copy-paste)

• “Rewrite using absolute counts per 100 people and state the budget impact in one sentence.”
• “Red-team your summary: list the 2 most plausible alternative explanations and how to rule them out cheaply.”
• “If multiple comparisons were run, add one sentence on the increased false-positive risk.”

Metrics to track (make results visible)

• Decision lead time: minutes from share to decision (target: under 15 minutes).
• Rework rate: % of summaries needing clarification (target: under 10%).
• Next-step adoption: % of summaries that trigger the recommended pilot (target: 60%+).
• Meeting time saved: minutes cut from review meetings (target: 20–30%).
• Clarity score from readers (1–5) on a one-question pulse (target: 4.5+).

Common mistakes & fixes

• Mistake: Treating p ≈ 0.05 as a green light. Fix: Enforce the threshold rule and label as “suggestive” if CI spans zero.
• Mistake: Only relative lifts. Fix: Require absolute counts per 100 and units.
• Mistake: Causality creep. Fix: Instruct: “Describe association only unless randomized.”
• Mistake: Missing baseline. Fix: Add a baseline rate or ask the AI to request it.
• Mistake: One-size-fits-all tone. Fix: Use the two-version output (exec + frontline).

One-week action plan

1. Day 1: Pick 3 recent analyses. Define a simple threshold for each (effect size + CI rule).
2. Day 2: Run the Stat Card prompt for all 3. Capture time-to-decision and clarity scores.
3. Day 3: Add the red-team step. Convert all effects to absolute per 100. Reduce rework.
4. Day 4: Standardize a one-slide template with the 3 outputs (decision, confidence, next step).
5. Day 5: Brief your team (15 minutes). Assign someone to check CI vs. threshold before shipping.
6. Day 6: Use the output in your next exec meeting. Track meeting time saved.
7. Day 7: Review metrics, tighten the threshold rules, and create a small library of filled examples.

Bottom line

Keep the loop, but level it up with thresholds, absolute numbers, and self-critique. You’ll cut decision time, reduce rework, and make every summary actionable.

Your move.

[Author]

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