Preface

The first 60 years of manned space exploration has seen great advances in technology and achievement. Not the least of these are the great leaps in knowledge made through observation and experimentation in the biological and medical sciences. Though the United States National Aeronautics and Space Administration (NASA) completed twelve crewed missions to the moon between 1969 and 1972, the vast majority of our knowledge of human physiology and response to space flight comes from extended stays in low Earth orbit on the space station Mir, the Space Shuttle, and the International Space Station.

NASA has expressed its intention to conduct crewed expeditions to the moon in the 2020s, working toward the goal of humans visiting Mars in the 2030s. The extended duration and distance from Earth these missions entail pose a number of new challenges for space agencies seeking to send humans into the abyss and return them safely home to Earth. New knowledge and new technology will be needed to conquer these challenges.

This book presents a small sample of the physiological changes and human health risks that have been observed in low Earth orbit, and that will undoubtedly be magnified with extended exploration operations to deep space. This book presents the evidence to date and offers a glimpse at what will be needed to take humanity further into deep space than ever before.

> **Robert J. Reynolds, MS MPH PhD** Mortality Research and Consulting Inc., Translational Research Institute for Space Health, Baylor College of Medicine, USA

**1**

Section 1

Introduction

Section 1 Introduction

**3**

**Chapter 1**

**1. Introduction**

space medicine.

Introductory Chapter: Research

Space Exploration

*Robert J. Reynolds and Mark Shelhamer*

afford the freedom to learn without using statistics at all.

**2. The problems of small-n settings**

can lead to difficulty in interpreting results.

**2.1 Violations of frequentist assumptions**

Methods for the Next 60 Years of

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

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

Research on small sample sizes poses a number of challenges. First and foremost is the violation of assumptions that frequentist statistical methods often require in order to be valid. Secondary to this, but inherent in the nature of small samples, is the typical lack of statistical power for detecting differences other than those in low-variance settings or those with dramatic effects. Each of these two challenges

Most frequentist statistical analyses follow a familiar pattern: assume the outcome follows a known statistical distribution, then test whether or not the observed data are unusual (unexpected) under the null hypothesis. However, beyond basic goodnessof-fit considerations, such analyses require other assumptions as well, many of which are clearly violated much of the time. Perhaps the most important of these assumptions is that the observations in a given sample are "iid"—independent and identically
