Statistical Communication: How to Explain Your Model to Non-Statisticians

Good statistical work deserves to be understood. Here are my favorite strategies for making complex ideas accessible:

1. Start With the Question

Instead of describing your model first, start with: “We wanted to know if…”. This keeps the focus on the goal, not the math.

2. Say What the Model Does

Instead of “We fit a hierarchical logistic regression with shrinkage priors”, say “We used a model that accounts for uncertainty across hospitals while still allowing us to compare them fairly.”

3. Use Uncertainty as a Strength

Most people think “uncertainty” means you're not sure. Frame it as what we know and how confident we are — show ranges, not just point estimates.

4. Translate Terms into Stories

If your model uses latent classes, say: “We grouped patients into categories based on their symptom patterns — even if we don’t directly observe their disease.”

5. Be Visual When You Can

Replace tables with visual summaries. A clean bar plot with confidence intervals speaks volumes.

6. Pre-answer the “So What?”

Always end with what your analysis means for decision-making, policy, or practice. Anticipate what a clinician, administrator, or policymaker might ask.