**4. Summary and conclusions**

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 assumptions necessary for valid models, much less valid inference.

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

Alternative methods of analysis can be used to aid our understanding in small-data situations. Bayesian methods, more sophisticated uses of well-known statistical methods, and methods from data science all provide useful techniques that work well with small datasets, provided the scientist is willing to think differently about the outcomes of these analyses.

It is our hope that researchers involved in space medicine will adopt these perspectives and methods. To the extent that these ideas and techniques are adopted by the broader research community, we expect to see great advancements in our knowledge of health and safety in spaceflight. It is this expansion of our collective knowledge that will help make possible the space exploration missions of the next 60 years.
