# Beyond Scientific Methods By:: [[Brian Heath]] 2024-04-17 One will find endless contradictions if one explores the widespread coverage of nutrition health. This is well-known. Sometimes eggs are good, and sometimes eggs are bad. What needs to be more well-known is why these contradictions exist. Part of the problem is reducing the news to bite-size tidbits for widespread consumption. This often leaves out the many caveats and nuances of the study's outcome. For example, the study might suggest that eggs are bad for humans, but the population only included those over 80 with diabetes. All the news outlets see and report is the headlines that eggs are bad, but rarely do they report the over 80 with diabetes bit. However, there are other, more essential causes of the contradictions, as nearly identical studies often produce different results in nutrition and health. The cause of the different results comes down to two key factors. First, humans and nutrition are highly complex and deeply individual. It is nearly impossible to conduct an accurate randomized study, control for all the variables, and get humans to comply fully. The tried-and-true scientific experimental methods fall apart. Thus, most turn to some version of epidemiological studies to find answers. Unfortunately, these studies are full of issues that cause literal statistical paradoxes, biases, and causal confusion. Essentially, epidemiological studies survey portions of the population and attempt to find correlations that might be insightful. For example, an epidemiological study will often find that people who are overweight drink diet sodas. Thus, one may naively conclude that diet sodas make people fat. However, it is more likely that those who are overweight drink diet sodas because they are overweight in an attempt to lose weight. This happens all the time, but many only see the first conclusion reported in the news. This is bad logic and, generally, bad reporting and bad science. Yet, it spreads like wildfire and generates economic value for all parties involved. While nutrition health is complex, consider related and even more complicated areas that plague humanity with uncertainty: psychology, economics, and society. It's even harder to run randomized experiments in these fields for pragmatic and ethical reasons. So, is it any wonder no one can agree on good and bad policies for healthcare, economics, and society? Reading the correlations, assuming causation, or getting it completely backward is a straightforward trap. If one is serious about solving systemic issues related to things like mental health and poverty via data, one needs to find alternatives beyond what science has to offer. One must experiment with computational simulation models to understand these perplexing issues. #### Related Items [[Science]] [[Statistics]] [[Experiments]] [[Paradox]] [[Society]] [[Economics]] [[Psychology]] [[Correlation vs Causation]]