This seminar bridges that gap. Designed specifically for non-statisticians, it provides a clear, practical introduction to biostatistics without overwhelming you with mathematics. You’ll learn the “why” behind the numbers, gain tools to spot meaningful findings, and avoid being misled by misused statistics. By the end of the program, you’ll be able to read statistical reports with confidence, communicate findings to colleagues and stakeholders, and make better data-driven decisions that can shape the future of clinical and biotech innovation. By attending, you will: Develop a clearer understanding of common statistical terms and concepts, Gain practical insight into how and why certain tests are applied, Learn approaches for distinguishing between statistically significant and clinically meaningful results, Improve your ability to follow discussions with statisticians and regulatory professionals.
Topics: statistics, clinical research, decision making, p-value, drug development, procedure development, statistical theory, statistical application, statistical software, non-statisticians, study design, interpretation of findings, biostatistics, statistical concepts, statistical skills, clinical research findings, statistical interpretation, non-mathematical introduction, descriptive statistics, measures of variability, confidence intervals, effect sizes, clinical significance, meaningful significance, comparative tests, correlation, regression analysis, non-parametric techniques, bayesian logic, bayesian methods, diagnostic testing, genetics, team exercise, scientific paper review, reproducibility, transparency, bias, limitations, statistical jargon, study power, sample size, statistical analysis plan, fda guidance, sap template, logistic regression, survival curves, cox regression, holistic study design