Our approach
From question
to evidence
Good statistics is more than a calculation. It’s a disciplined path from the decision you face to conclusions you can defend — and we make that path clear at every step.
Principles
How we think about your data
Start with the decision
Statistics is a means, not the goal. We begin with the decision you need to make and the risk you’re managing, then work back to the method and the data required.
Build for scrutiny
Assumptions are stated, sample sizes are justified, and methods are documented — so the analysis is transparent to an auditor or reviewer, not a black box.
Explain in plain language
A result nobody can act on has no value. We translate the statistics into clear conclusions your engineering, quality, and regulatory teams can use directly.
Right-size the work
The simplest method that credibly answers the question is the best one. We won’t over-engineer an analysis — or cut corners where rigor matters.
The engagement
A five-stage path, tailored to your project
Every engagement is scoped to the problem — but the shape is consistent, so you always know what happens next and what you’ll have in hand at the end.
- 01
Discover & scope
We learn your process, the question behind the data, and the regulatory context. You get a clear picture of the approach, effort, and deliverables before work begins.
- Problem and decision framing
- Regulatory / quality context review
- Proposed method and scope
- 02
Design & plan
Where data doesn’t yet exist, we design the study — the experiment, sampling plan, or protocol — and size it so the conclusions will be adequately supported.
- Experimental / sampling design
- Sample-size & power justification
- Analysis plan and acceptance criteria
- 03
Analyze
We run the analysis with the appropriate methods, verify assumptions, and pressure-test the findings — checking sensitivity, outliers, and alternative explanations.
- Method execution and validation of assumptions
- Sensitivity and robustness checks
- Reproducible, documented analysis
- 04
Interpret & document
You receive results, a plain-language interpretation, and a written rationale — figures, tables, and the statistical justification your teams can put straight into records.
- Clear findings and interpretation
- Reviewer-ready statistical rationale
- Figures, tables, and summary report
- 05
Support & defend
When questions come from an auditor, notified body, or agency reviewer, we help you respond — clarifying the methods and standing behind the analysis.
- Audit and submission support
- Responses to reviewer questions
- Follow-on analysis as needed
Standards & frameworks
Methods aligned with what your reviewers expect
We work within the frameworks that govern medical device and pharmaceutical manufacturing, so the statistics fit cleanly into your quality system and submissions.
FDA Process Validation
Stage 1–3 lifecycle approach
ICH Q8–Q11
Development & quality by design
ICH Q1E
Stability & shelf-life evaluation
ISO 13485 / 14971
Design controls & risk context
AIAG MSA
Measurement systems analysis
ANSI/ASQ Z1.4 & Z1.9
Attribute & variables sampling
ASTM E2587
Control charts for SPC
Six Sigma / DMAIC
Structured improvement
Frameworks referenced describe the context our analyses support; alignment does not imply certification or endorsement by these bodies.
Tools
Validated methods in the software you already trust
We deliver analysis in the environment that best fits your team — from open-source R and Python to JMP and Minitab — with reproducible, well-documented work. When it helps, we package it as an interactive Shiny or Streamlit app, or a custom Minitab/JMP macro your team can reuse.
- R
- Python
- JMP
- Minitab
- Excel
Start a conversation
Have a data question you need answered with confidence?
Bring your product or process data challenge. In a short introductory call we’ll scope the statistical approach and the evidence you’ll need.