Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration two weeks or more before the course is scheduled to begin, you are entitled to receive your choice of either a credit for a future seminar (which can be applied toward any of our courses) or a refund of the registration fee (minus a processing fee of $50). 

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 3-Day Livestream Seminar Taught by Marcella Boynton, Ph.D.

Experimental research—particularly randomized controlled trials (RCTs)—is widely regarded as the most scientifically rigorous approach for evaluating interventions in clinical, public health, and social science settings. Professionals involved in designing, implementing, or interpreting these studies often come from diverse backgrounds, and may have had limited formal training in the statistical methods used to analyze trial data. A clear understanding of these methods is essential because the credibility and utility of trial findings depend not only on how the study is conducted, but also on how the data are interpreted.

This workshop offers both a conceptual foundation and practical tools for analyzing data from RCTs and other experimental designs. Whether you are a clinician, public health practitioner, ethics specialist, policy analyst, or social scientist working with experimental or quasi-experimental data, this course will help you build—or strengthen—your core statistical skills in a supportive, applied setting. No prior statistical experience is expected.

Topics include experimental design and RCT principles, descriptive statistics for summarizing samples, analysis of continuous (t-tests, ANOVA, linear regression) and categorical (chi-square tests, odds ratios) outcomes, assessment of diagnostic tests (sensitivity, specificity, predictive values), and an introduction to survival analysis. The course focuses on the practical “when and how” of conducting statistical analyses, with hands-on examples using real-world health data.