How to Science: Research vs. Real World Evaluation

What is science? Some may picture someone like Albert Einstein, whilst others may picture white-coated scientists working away in a lab. Science can be arranged into two broad categories: natural and social science. Natural sciences encompass disciplines such as chemistry, physics, biology, and other natural phenomena studies. Social sciences incorporate disciplines such as psychology, economics, and others attributed to the science of people. As theories and observations are the two pillars of science, scientific research functions at two levels: a theoretical and an empirical level.

Within the paradigm of this blog, research could equate to the theoretical level of science. Research is concerned with developing abstract concepts and understanding relationships between concepts within a controlled environment (i.e., to build “theories”). In contrast, real world evaluation can be viewed as the empirical level of science, whereby the focus is testing the possible theoretical concepts and relationships to consider how well they reflect our reality (i.e., assessing observations within an environment that is less strictly controlled). Both levels should still conform with the scientific method: (1) replicability, (2) precision, (3) falsifiability, and (4) parsimony.

If we use the analogy of a runner undergoing rigorous training for a performance test. The runner, or scientist, has different training options at his disposal. For example, the runner could either use a treadmill (theoretical), or perform trail runs (empirical) to prepare for the performance test.

On the treadmill, most variables are controlled by the runner. This includes speed, incline, time spent exercising (the runner can stop at any time without have to suffer the consequences of a long walk home), and even environmental conditions such as temperature (using a fan) and weather changes.

A trail run would be slightly different. Even though the activity, to run, is the same; the runner can only control certain variables. For example, the running route. Whilst the runner has an idea of how far the route is, the likely incline, an idea of how long it will take, and the current weather; these factors remain slightly variable in reality. It could have heavily rained the night before, leaving the path muddy and making it more difficult to keep the intended pace.

So, if you were the runner, how would you decide which training option is the best? The training option that the runner selects is dependent on the nature of the performance test, or the evaluation question at hand. If the runner wanted to improve performance on an exercise stress test, the treadmill would be the better suited option, as it could allow for an increased level of monitoring and control (for example, the Bruce protocol to be carried out, attachment of ECG leads, etc.). Whereas if the runner were training for the Zugspitz Ultratrail (an ultra-trail run in the Bavarian mountains ranging from 25km to 106 km), the trail run method would be the best training option due to the use case.

Science involves continually moving back and forth between theory and empirical evidence; both levels are important components of science. In reality, scientists are constrained by factors such as time and resources; therefore, the most viable level of science for a specific project can be defined by the all-important research question.