We're a small, specialized team of PhD biostatisticians with over two decades in federal research. Whether you're designing a multi-site trial or need an independent review before submission, we bring the analytical rigor — and speak your language.
We don't just run models — we help you choose the right design, the right method, and the right interpretation. Here's what that looks like in practice.
We help you ask the right question and design the study to answer it — from power analysis and randomization schemes to full statistical analysis plans (SAPs) using mixed models, GLMs, and DOE frameworks like split-plot and RCBD.
From Bayesian hierarchical models to random forests and penalized regression, we match the right method to your data — not the other way around. Fluent in R, Python, SAS, and SQL across high-variability, small-sample biological datasets.
We know how federal research works — from USDA-ARS multi-location trials to SBIR proposals. Our deliverables are audit-ready, compliant with agency documentation standards, and built for peer review scrutiny.
We've trained hundreds of researchers and program managers on everything from interpreting ANOVA tables to building reproducible analysis pipelines. Custom workshops in R, SAS, JMP, and core statistical methodology — tailored to your team's level.
You don't need another analytics vendor. You need a collaborator who understands your science and can translate it into sound statistical practice.
20+ years as Area Statistician for USDA-ARS — we understand the pace, politics, and rigor of government-funded science
Collaborative experience across 22 research locations, distributed teams, and multi-year study designs
We don't need a crash course in your field — our background spans life sciences, ag systems, genomics, and environmental research
Every analysis is version-controlled, documented, and built to withstand peer review, audits, and replication
Every engagement follows a structured, transparent path — so you know exactly where your project stands and what comes next.
We start by understanding your research objectives, constraints, and what a successful outcome looks like — whether that's a power analysis for a new trial or a modeling strategy for existing data.
We develop a statistical analysis plan (SAP) tailored to your study — selecting appropriate designs (split-plot, RCBD, repeated measures) and specifying models, assumptions, and validation approaches up front.
Analysis runs in reproducible, version-controlled code (R, Python, or SAS). We fit the agreed-upon models — mixed-effects, GLMMs, Bayesian hierarchical, or ML pipelines — and run diagnostics at every stage.
You receive clear, actionable results — not just p-values and tables. We deliver annotated reports, visualizations, and documented code so your team (or reviewers) can reproduce every finding.
Whether you're scoping a new study, need a second set of eyes on an analysis plan, or want a long-term statistical partner — we'd enjoy the conversation.
Based in Virginia, serving clients worldwide