Why this matters
As an Applied Scientist, you turn experiments, models, and analyses into business and product decisions. Clear research summaries help:
- Product managers understand trade-offs and choose a path.
- Engineers plan rollouts based on measured impact and risk.
- Leaders align on investment, timelines, and success metrics.
- Teammates reproduce work without misinterpretation.
Common real tasks: weekly experiment updates, model improvement summaries, launch docs, model cards, postmortems, and grant/roadmap rationales.
Who this is for
- Applied Scientists and ML Engineers who share findings with non-research stakeholders.
- Data Scientists translating experiments into product impact.
- Researchers preparing concise updates, abstracts, or executive summaries.
Prerequisites
- Basic understanding of experimental design and metrics (A/B tests, confidence intervals, effect sizes).
- Ability to read your own results (tables, charts) and extract key numbers.
Concept explained simply
A research summary is a short, decision-ready narrative: what you tried, what happened, how sure you are, what it means, and what you recommend.
Mental model
Think of your summary as a guided funnel:
- Start broad: the problem and goal in one sentence.
- Deliver the headline result with 1–3 key numbers.
- Show uncertainty and caveats honestly.
- Translate into implications and next steps.
A reliable structure (condensed IMRaD)
- Context: One sentence on the problem and why it matters now.
- Objective: What question did you answer?
- Method (very brief): How you tested it (design/data).
- Results: Headline numbers, direction, magnitude.
- Uncertainty: Variability, significance, limitations.
- Implications: What changes, who benefits, trade-offs.
- Recommendation: Clear next action with decision criteria.
Example skeleton (copy-paste template)
Problem: [1 sentence] Objective: [1 sentence] Method: [design/data, 1 sentence] Results: [top 1–3 numbers] Uncertainty: [CI, p-value, sample size; key caveats] Implications: [impact on KPI, users, costs] Recommendation: [do X if Y is true; else do Z].
Worked examples
1) A/B test on onboarding
Before (too long, unclear)
We ran a 14-day test for a new tooltip flow. There were multiple variants and some users saw different sequences depending on device. We noticed a positive trend for activation but not uniformly across all segments...
After (clear, decision-ready)
Objective: Improve new-user activation. Method: 14-day A/B test (N=82k), variant adds two context tooltips. Result: Activation +2.1 pp (from 43.6% to 45.7%), p=0.03; mobile +3.4 pp, desktop +0.2 pp (ns). Uncertainty: No impact on churn; slight signup time increase (+6s). Implication: Gains concentrated on mobile. Recommendation: Roll out to mobile; keep desktop as is, and test copy there.
2) Model upgrade trade-off
Before (jargon-heavy)
Switching to V2 gives a 1.2% absolute AUC lift with a 4x parameter increase and longer latency; calibration improves on tail cohorts but may regress on cold-start.
After (clear, balanced)
Objective: Increase relevance without hurting UX. Method: Offline + online holdout. Result: AUC +0.012 (0.794 → 0.806), CTR +1.4% (CI [+0.6%, +2.1%]); p50 latency +18 ms, p95 +40 ms; compute +22%. Implication: Small but consistent engagement gain, modest latency hit. Recommendation: Ship V2 behind a latency guard (p95 < 240 ms); if exceeded, auto-fallback. Revisit compute budget next quarter.
3) Causal analysis for price change
Before (buried lead)
Using difference-in-differences we found mixed results across regions; some had higher retention, some had lower, which may relate to marketing seasonality...
After (focused)
Objective: Estimate retention impact of a 5% price increase. Method: Difference-in-differences across 12 regions with matched controls. Result: 30-day retention −0.7 pp (CI [−1.2, −0.2]), revenue +3.2% (CI [+2.1, +4.3]). Implication: Revenue increase outweighs small retention dip. Recommendation: Proceed, but exclude Student plan (retention −2.4 pp) and monitor cohort quality for 8 weeks.
Step-by-step method
- Identify the reader: PM, engineer, exec, partner team.
- Write a one-sentence purpose: “We tested X to improve Y.”
- Pull 1–3 headline numbers: effect, direction, magnitude.
- Add uncertainty: CI, p-value, N, or variance bands.
- Name the main caveat: bias, missing data, external validity.
- Translate to impact: KPI movement, users affected, cost/latency.
- Recommend an action with a guardrail: “Ship if p95 latency < T.”
- Trim to fit: 150–200 words max unless a full report is needed.
Micro-style rules (quick wins)
- Use active voice: “We tested…”, “The model improved…”.
- Prefer concrete numbers to adjectives: “+2.1 pp” over “better”.
- Round sensibly: 2–3 sig figs unless precision matters.
- State units and baselines: “from 43.6% to 45.7%”.
- Be explicit about scope: “mobile only”, “US cohort”, “Q4 data”.
- One idea per sentence. Keep sentences < 22 words when possible.
Summary quality checklist
- [ ] Audience and objective are clear in the first two sentences.
- [ ] Top 1–3 numbers are front and center with units and baselines.
- [ ] Uncertainty and sample size are stated.
- [ ] Caveats/limitations are honest and specific.
- [ ] Actionable recommendation and guardrails included.
- [ ] Length <= 200 words; plain, concrete language.
Common mistakes and how to self-check
- Hiding the lead: If your main number appears after paragraph 2, move it up.
- Overclaiming certainty: If there is no CI/N, add it or qualify the claim.
- Jargon without purpose: Replace or define in parentheses once.
- Vague impact: Tie results to a business/user metric or decision.
- Missing trade-offs: Name the cost (latency, compute, complexity).
Self-check prompt you can use
“In 30 seconds, can a PM tell what changed, by how much, how sure we are, and what to do next?” If not, edit until the answer is yes.
Exercises
Do these in order. Answers are available to everyone; saved progress requires being logged in.
Exercise 1 (mirrors Ex1): Executive summary from raw test notes
Write a 120–150 word summary for a PM from the scenario in Exercise 1 below.
Exercise 2 (mirrors Ex2): Create a 2-sentence TL;DR
Compress the noisy paragraph into a crisp TL;DR for stakeholders.
Exercise 3 (mirrors Ex3): Report metrics with uncertainty
Choose the right metrics and uncertainty framing, then write a one-paragraph summary.
Practical projects
- Rewrite three past experiment updates into the structure above; ask a PM for 1-minute feedback.
- Create a model change summary with a rollout decision tree (ship if/hold if) and guardrails.
- Build a personal summary template snippet you can reuse in docs and PRs.
Learning path
- Master the structure: practice with the provided template.
- Numbers with meaning: always include baselines, units, and uncertainty.
- Decision orientation: add implications and guardrails to every summary.
- Speed and polish: practice writing under a 10-minute timer.
- Peer review: swap summaries with a PM or engineer and iterate.
Next steps
- Complete the exercises below, then take the Quick Test.
- Apply this format to your next real update or launch doc.
- Set a reminder to review and refine your personal template monthly.
Mini challenge
In 90 words or fewer, summarize an experiment you ran last month. Include: goal, top result with numbers, uncertainty, and a recommendation. Time yourself for 7 minutes.