*3.2.1 Alternative uses of common statistical models*

With a good understanding of common statistical models, it is possible to exploit their properties to conduct atypical investigations. Here we use an example from the literature on astronaut mortality to demonstrate this idea.

Using data on US astronauts and Soviet and Russian cosmonauts, Reynolds et al. [5] demonstrated that mortality from cancer and cardiovascular disease have no common causes in this population. This in turn was taken as evidence that doses of ionizing radiation received in space cannot have been sufficient to affect mortality from both of these causes. This was achieved by showing that a naïve analysis of survival curves (where competing causes of death were treated as censoring events) were not markedly different from survival curves that account for competing risks. That is, the causes of death displayed statistical independence which, in DAG terms, means they share no common ancestor.

In this example, the authors exploited the implications of different statistical methods for computing survival in presence of competing risks to make inferences regarding the structure of causal relationships. This is but one example, and undoubtedly others exist for those who can think broadly and conceptually about specific questions to be asked of existing datasets.

### *3.2.2 Simulation*

The advancements in computing power over the last several decades have made possible more sophisticated forms of analysis, not least among them being simulation. We refer here to several different well-established approaches, all of which have found use in various domains such as statistics, business, and engineering.

Markov-chain Monte Carlo simulation (MCMC) has been used for decades in engineering for probabilistic risk assessment. Agent-based simulation has found increasing popularity in epidemiology for modeling community-level effects of policy

**7**

*Introductory Chapter: Research Methods for the Next 60 Years of Space Exploration*

change or change in social environment. Techniques such as the bootstrap and the jackknife may be loosely grouped here as well, as they rely upon repeated recalculation of sample statistics using algorithms that resample the data in specific ways. Finally, simple "what-if" analyses can help find the extremes of what is possible in a process or phenomenon, and can be used to eliminate possibilities or competing hypotheses.

Though certainly not new, Bayesian methods are still underutilized in research in general and in space medicine in particular. This is primarily owing to the unfamiliarity of most researchers with these methods, which in turn is due to the lack of graduate-level training on them in most scientific programs other than statistics. Historically, this was sensible: their mathematical complexity and need for computing power made them difficult to implement for all but the simplest of applications. Fortunately, computer science and computer hardware have both evolved to where these methods are easy to implement, creating a large opportunity for researchers to work with smaller datasets in meaningful and rigorous ways without reliance on

In recent years, Data Science has been turning business analytics upside down. In general, data science is understood as the science of learning from data, a seemingly perfect fit to our objectives here. Yet Data Science has seen much slower adoption in Academia, perhaps owing to the fact that the only part of Data Science that fits with the traditional epistemological approach to research is that part of

A hallmark of Data Science is the use of machine learning. However, many of the methods of machine learning are methods that typically benefit from large datasets: those with hundreds of columns and millions of rows. Nevertheless, machine learning does have techniques that can be of use in the small-n world. Techniques for data reduction, data visualization, data mining, and simulation all are powerful tools that can often be applied in the domain of small-n research. Perhaps of particular interest to space medicine, researchers are able to use these methods for exploratory data analysis and hypothesis generation, tasks at which unsupervised machine learning excels.

In this chapter we have discussed the limits of NHST as a surrogate for a broader, more flexible epistemological framework. Over-reliance on NHST can cripple the research enterprise when sample sizes and sampling schemes fail to conform to the

A motivating factor for the use of NHST is the desire to draw correct conclusions. This is a valid aim, but may lead to an emphasis on error avoidance at the expense of learning from (possibly limited) data. Instead, scientists need to consider evidence using Hill's guidelines for causation, should examine whether or not the data in hand conform to or defy the assumptions needed for causal inference, and should include the use of DAGs to better understand what we already know about a given topic, and to clarify what we conjecture to be true *a priori*. Formulating so-called "alternative" hypotheses appropriate to the topic under study may even allow us to improve our inferences when using traditional NHST. There is

assumptions necessary for valid models, much less valid inference.

no need to restrict ourselves to one approach or the other.

Data Science which uses traditional NHST statistics.

**4. Summary and conclusions**

*DOI: http://dx.doi.org/10.5772/intechopen.92331*

*3.2.3 Bayesian methods*

NHST and p-values.

*3.2.4 Data science*

### *Introductory Chapter: Research Methods for the Next 60 Years of Space Exploration DOI: http://dx.doi.org/10.5772/intechopen.92331*

change or change in social environment. Techniques such as the bootstrap and the jackknife may be loosely grouped here as well, as they rely upon repeated recalculation of sample statistics using algorithms that resample the data in specific ways. Finally, simple "what-if" analyses can help find the extremes of what is possible in a process or phenomenon, and can be used to eliminate possibilities or competing hypotheses.
