**1. Introduction**

There are many potential health hazards inherent to space travel, and, as the chapters in this book make clear, even after 60 years of human space exploration, much is left to be learned about how to live and work in space. As a result of the diversity of problems that remain to be solved, the scientific methods required to research these issues need to be flexible and varied. This is perhaps most true in our approach to analyzing data and drawing conclusions from them in the context of space medicine.

In a commentary published in the Journal of Applied Physiology, Ploutz-Snyder et al. [1] point out that in the study of exotic topics (such as the physiology and health of space travelers) the available data are often insufficient to satisfy the sample-size requirements for traditional null-hypothesis statistical testing (NHST). They rightly point out that if we hold this as the standard of good research, (i.e., if NHST is our only, or even our preferred, tool for learning from data) we will be forced to abandon whole lines of research. While the authors offer several "approaches for justifying small-n research," even these are attempts to shoehorn small datasets into traditional statistical analysis. This misses the broader (epistemological) point: what is needed in small-n studies is not just a better way to use statistics, but rather other tools which afford the freedom to learn without using statistics at all.
