Through the development of a model to study HPV and oral cancer, this How-to Talk will discuss the choices and considerations that you can make to develop a cell culture model that better mimics disease processes.

We’ve all heard about the serious limitations of frequentist statistics: p-hacking, misinterpreted results, and unmet assumptions of normality, etc.. Bayesian statistics presents an alternative. In Bayesian statistics, using the powerful simplicity of Bayes’ theorem and modern computational muscle, the credibility of candidate parameter values is robustly reallocated to be most consistent with the data. In this seminar, after a brief introduction to the fundamentals of Bayesian statistics, we will apply our knowledge to that workhorse of experimental design: a comparison between two groups. We will execute both a standard t-test and a Bayesian estimation to determine the difference between groups, then compare the results.