Weronika Dardzińska, PhD, DVM is a pharmacovigilance and clinical-development leader with over 15 years of global experience in drug‑safety science, AI‑driven data review and front‑line patient care. She has deep therapeutic expertise in oncology (NSCLC, multiple myeloma) and respiratory disease (COPD, asthma), gained through safety roles at GSK, MSD and TFS. Weronika pairs her doctorate with postgraduate training in epidemiology, biostatistics, computational biopharmacy and pharmacoeconomics/HTA, weaving real‑world evidence and population models into drug-safety decision‑making. Her current research interest lies in translating advanced analytics into measurable improvements in patient quality of life and outcomes.
In pharmacovigilance (drug safety), we rely on data to detect and assess adverse drug reactions. But data can mislead if we’re not careful - even if you have a statistician on your team. There are certain statistical pitfalls (“stat traps”) that drug safety professionals may fall into when interpreting safety data. Often, a busy statistician might not explicitly warn you about these, assuming they’re understood. Below we’ll explore seven of the most common stat traps in pharmacovigilance and how to avoid them, using clear examples and storytelling to make the statistics more approachable and memorable. No formulas- just clear visuals and practical guardrails.