Biostatistics for the Non-Statistician

Topic Description:

Statistics can be described as the science of making decisions in the face of uncertainty. Nowhere is this more important than in clinical research and biotechnology, where the conclusions drawn from data can determine whether a new treatment advances, a regulatory application succeeds, or a scientific paper influences practice. For professionals without formal training in statistics, the numbers, formulas, and jargon can feel daunting. Yet, the ability to understand and communicate statistical findings is no longer optional — it is essential.

This two-day seminar, Biostatistics for the Non-Statistician, was created to bridge the gap between statistical theory and practical application. Its goal is not to turn attendees into statisticians, but to provide enough knowledge to confidently interpret results, recognize when statistics are being misapplied, and communicate findings with clarity. Across lectures, discussions, and exercises, the program emphasizes understanding, application, and interpretation, not memorization of formulas.

Day 1 introduces participants to the fundamental building blocks of statistical reasoning. The day begins with an exploration of why statistics matter in research and healthcare decision-making. Participants will examine the essential distinction between samples and populations, learn why variability and uncertainty must be accounted for, and gain insight into what statistics can — and cannot — accomplish. By dispelling the myth of the statistician as a “magician,” this session underscores the value of clear, transparent methods over mysterious or opaque analyses.

The day continues with an in-depth look at the many ways data can be interpreted. Key concepts such as p-values, effect sizes, and confidence intervals are explained in plain language, along with the differences between statistical and clinical significance. By demystifying these measures, attendees will be better equipped to recognize whether findings are not only statistically valid but also practically meaningful in real-world contexts.

Attention then turns to the application of common statistical tests. Participants will learn why testing is necessary through the lens of Null Hypothesis Significance Testing (NHST), and will gain familiarity with comparative tests, correlation methods, multiple regression analysis, and non-parametric techniques. Rather than teaching calculations, this session emphasizes understanding when and why each method is used, along with the strengths and limitations of each approach. The day concludes with an introduction to Bayesian logic, which offers a different perspective on statistical thinking. Attendees will see how Bayesian methods can provide richer insights into diagnostics, genetics, and other areas where uncertainty plays a central role.

Day 2 builds on this foundation of Day 1 with applied and forward-looking topics. The day begins with a guided exercise in interpreting systematic reviews, a cornerstone of evidence-based medicine. Participants will learn how to evaluate statistical language across multiple studies, identify sources of bias, and judge the transparency and reproducibility of reported findings. Emphasis is placed on developing the ability to communicate insights clearly to both technical and non-technical audiences.

The next session explores study power and sample size — critical considerations in trial design and regulatory submissions. Attendees will review how concepts such as p-values, effect size, and significance levels come together in sample size calculations, and will be introduced to available formulas, software tools, and practical resources for applying these concepts in real projects.

Then, participants will learn the essential steps in developing a Statistical Analysis Plan (SAP). Using FDA guidance as a foundation, the session walks through how to design a plan that ensures clarity, compliance, and rigor. A template SAP is provided to give attendees a concrete starting point for their own work.

The seminar closes with a discussion of specialized topics and an open Q&A. Here, participants will explore survival analysis methods, applications in pharmacokinetics and pharmacodynamics, and strategies for taking a holistic view of study design and interpretation. This final session ties together the threads of the seminar, encouraging participants to approach data with both confidence and critical thinking.

By the end of the two days, attendees will not only understand the language and logic of biostatistics but will also be able to apply these concepts to their daily work. They will leave better prepared to evaluate published research, collaborate with statisticians, develop study plans, and communicate statistical findings clearly to colleagues, regulators, and other stakeholders. Most importantly, they will gain the assurance that statistics are not a barrier, but rather a powerful tool to improve decision-making and advance clinical and scientific discovery.