Learning in Medicine: The Importance of Statistical Thinking

Abstract

In many fields, including medicine and biology, there has been in the last years an increasing diffusion and availability of complex data from different sources. Examples include biological experiments or data from health care providers. These data encompass information that can potentially enhance therapeutic advancement and constitute the core of modern system medicine. When analyzing these complex data, it is important to appropriately quantify uncertainty, avoiding using only algorithmic and automated approaches, which are not always appropriate. Improper application of algorithmic approaches, which ignore domain knowledge, could result in filling the literature with imprecise and/or misleading conclusions. In this chapter, we highlight the importance of statistical thinking when leveraging complex data and models to enhance science progress. In particular, we discuss the reproducibility and replicability issues, the importance of uncertainty quantification, and some common pitfalls in the analysis of big data.

Keywords: Bias–variance trade-off; Fair decisions; Model assessment; Reproducibility and replicability; Uncertainty quantification.