English for Data Scientists: Presenting Results to Non-Technical Stakeholders

One of the hardest communication challenges in data science is translating technical findings into language that business stakeholders can act on. A model with 94.2% accuracy means nothing to a product manager who needs to decide whether to ship a feature. A p-value of 0.03 means nothing to a CEO.

This guide covers the specific English vocabulary, sentence structures, and communication patterns that data scientists need to communicate effectively with non-technical colleagues — in presentations, Slack messages, written reports, and meetings.

The Core Communication Challenge

Data scientists are trained to be precise. Business stakeholders need to be decisive. These goals are often in tension:

Data scientist mindset	Stakeholder mindset
”The model has 87% recall and 91% precision."	"Will this catch most of the fraud?"
"There is a statistically significant correlation."	"Should we change the product?"
"The confidence interval is 0.12–0.34."	"Is the result reliable enough to act on?”

The skill is not dumbing things down — it is translating technical findings into business language without losing accuracy.

Part 1: Vocabulary for Data Presentations

Replacing jargon with plain English

Technical term	Business-friendly equivalent
model accuracy	how often the system gets the prediction right
precision / recall	the false alarm rate / the miss rate
confidence interval	the range we expect the true value to fall in
statistical significance	whether the pattern is real or likely due to chance
p-value	the probability that the result is a fluke
overfitting	the model memorised the training data but doesn’t generalise
feature importance	which factors most influence the prediction
baseline model	the simplest comparison point — what the current system does
training vs inference	building the model vs using it in production
A/B test	a controlled experiment comparing two versions

Key phrases for introducing findings

“What this means in practice is…”
“In plain terms, the model correctly identifies X% of…”
“To put this in context…”
“The business implication is…”
“The key takeaway here is…”

Part 2: Presenting Model Results

Stating results clearly

❌ “The model achieves 0.91 AUC-ROC on the held-out test set with a threshold of 0.45.”

✅ “The fraud detection model correctly flags 89% of fraudulent transactions while generating a false alarm on roughly 1 in 200 legitimate transactions. In our current transaction volume, that’s about 40 false alerts per day — manageable for the review team.”

The second version:

States the result in terms of what it means for actual users
Connects to a business metric (review team workload)
Skips the technical threshold and AUC — those belong in an appendix

Structure for presenting a model result

What the system does (in one sentence): “This model predicts which users are at risk of churning in the next 30 days.”
How well it works (in business terms): “In testing, it correctly identified 3 out of 4 users who actually churned.”
What it misses / its limitations: “It has higher false-positive rates for new users with limited history.”
What it means for the business: “Targeting the top 500 at-risk users per week could retain an estimated [X] users per month.”

Part 3: Communicating Uncertainty

Data scientists often hedge too little (overstating confidence) or too much (making results seem unreliable). These phrases strike the right balance.

Expressing confidence appropriately

High confidence:

“The data strongly suggests…”
“We can say with high confidence that…”
“The pattern is consistent across all three datasets, which strongly indicates…”

Moderate confidence:

“The evidence points to…”
“The results are consistent with…, though we’d want more data to be certain.”
“This finding is promising, but should be validated on production data before we act on it.”

Low confidence / preliminary:

“This is an early signal — we’d treat it as a hypothesis rather than a conclusion.”
“The sample size here is small, so these results may not hold at scale.”
“We’re seeing an interesting pattern, but it could be an artefact of the data collection period.”

Explaining statistical significance without using the term

❌ “The difference is statistically significant at p < 0.05.”

✅ “We ran this across 50,000 user sessions. The difference between the two groups is large enough that we’re confident it’s a real effect and not just random variation.”

✅ “If we ran this experiment 100 times with random data, we’d expect to see a result this large less than 5% of the time. So we believe this is a real effect.”

Part 4: Written Reports and Slack Updates

Experiment summary (Slack)

**Churn Prediction Model — Experiment Summary**

What we tested: A new ML model that predicts 30-day churn risk
Test period: Feb 1–28 (47,000 users split 50/50)

Results:
• Users shown personalized retention offers: 9.2% churned
• Control group (no change): 12.1% churned
• Relative reduction: ~24%

Confidence: High — the result is consistent across all user segments we checked

Next step: I'd recommend expanding to 100% of at-risk users from April 1.
Happy to share the full analysis if useful.

Data report structure (written)

A clear data report to stakeholders follows this structure:

TL;DR / Executive Summary (2-3 sentences, the key finding and recommendation)
Background (why we ran this analysis, in 2-3 sentences)
What we found (key results in plain language)
Limitations (what the data cannot tell us)
Recommendation (what to do next, with clear rationale)
Appendix (technical details, charts, statistical tests — for those who want them)

The executive summary goes first — stakeholders often read only that.

Part 5: Handling Difficult Questions

When the data is inconclusive

“The results are mixed — we see the effect in mobile users but not on desktop. That suggests [interpretation]. I’d recommend [next step] before drawing a firm conclusion.”

“We don’t have enough data yet to say definitively. Based on the current sample size, we need roughly [X] more weeks of data to detect an effect of this size. Until then, I’d treat this as a hypothesis.”

When the model is worse than expected

“The initial results came in below our target. We’ve identified two likely causes: [cause 1] and [cause 2]. We’re addressing [cause 1] this sprint and expect to see improvement by [date]. Would it be helpful to schedule a review after we’ve made those changes?”

When you’re asked to make a recommendation but the data doesn’t support it

“Based on this data alone, I can’t recommend one direction over the other — the difference is within the margin of error. If we need to make a call now, I’d suggest [option] because [intuition/business reason], but I’d treat it as a low-confidence decision and plan to review it in [timeframe].”

This is better than either:

Overstating confidence to give an answer
Refusing to engage and saying “more data needed” repeatedly

Part 6: Vocabulary Quick Reference

Situation	Useful phrases
Introducing the bottom line	”The short version is…”, “What this boils down to is…”, “The key finding is…”
Explaining what a number means	”To put that in context…”, “In practice, this means…”, “Translated to business terms…”
Hedging a finding	”The early signal suggests…”, “This is preliminary but…”, “We’d want to validate this before acting on it…”
Recommending action	”Based on this, I’d recommend…”, “The data supports…”, “The most defensible next step is…”
Deferring to domain expertise	”That’s ultimately a business judgment — the data shows X, but whether that’s acceptable is your call.”
Acknowledging a limitation	”One caveat here is…”, “This analysis doesn’t account for…”, “We should note that…”