For 15 years the scientific literature repeatedly underlines the important lack of reproducibility and replicability of studies in biomedical and psychological research. As a consequence, several scientific organizations (journal, scientific societies, universities, national agencies) have identified some root causes to this issue and proposed good research practices to improve the reproducibility of the results. The misuse of statistical concepts, from design of studies to analysis of data to decision-making is at the heart of the crisis, even if not the only cause. In this webinar we will explain how to understand the sequences of issues and how to fix it in order to drastically improve the replicability of the results. Through example the webinar will cover concepts such as OFAT vs DoE, p-values and Bayesian statistics and Power vs Assurance as easy opportunities to improve robustness of decisions.
Key learning objectives:
- The value of good design of experiments
- Consider Bayesian statistics to answer your question
- P-values is not always what you’re looking for
- Adopt a life-cycle over the long-run, not just study by study
