OnDemand: Statistics Behaving Badly: Common Pitfalls in Data Analysis and How to Avoid Them

Those involved in outcomes research, clinical research, or anything related to data analysis know that statistical tests are not one-size-fits-all. Yet it’s not always clear which test to use. There are dozens of common statistical tests, each with its own set of assumptions that must be met in order for the results to be valid. We hear this warning often, but what does it mean? What actually happens when assumptions are violated? And more importantly, how do we avoid these violations? Even when assumptions are met, there are other pitfalls in data analysis that can result in the wrong conclusions.

In our complimentary webinar – Statistics Behaving Badly: Common Pitfalls in Data Analysis and How to Avoid Them – Dr. Jamie Reiter reviews some of the more common mistakes seen in data analysis, with demonstrations to help illustrate the importance of using statistics wisely. She also dives into some of the real-world consequences of poorly used statistics. Topics include:

  • Parametric vs. nonparametric techniques
  • Paired vs. independent data
  • Familywise error rate
  • Confounding variables
  • Misinterpreting p-values
  • And a few additional not-so-savory practices.

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Presenter: Jamie Reiter, PhDAbout the Presenter:
Jamie Reiter, PhD is Chief Executive Officer and Founder of MedEvaluate, a statistical, research, and medical writing consulting firm whose ultimate goal is improving patient outcomes through sound experimental design, accurate data analysis, and effective communication. Dr. Reiter earned her PhD from the Department of Cognitive Sciences at UC Irvine, with an emphasis on data analysis and developing mathematical models for analyzing neuropsychological data in patients with dementia. It was there that she developed a passion for extracting meaning from data, using creative yet validated statistical and mathematical approaches. Over her career, Dr. Reiter has held director-level outcomes research and statistics positions at medical education companies; developed and taught introductory and intermediate biostatistics courses for UC San Diego Extension; presented several industry workshops and webinars on research and statistics; and worked as statistician, medical writer, and scientist for the pharmaceutical industry, academic and research institutions, medical societies, and contract research organizations.

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