One concept that I understand in a new way is data interpretation. In particular, I am starting to be more thoughtful about what can and cannot be concluded from data.
I began to think differently about the concept of data interpretation when we studied the honeybee papers in class. In one paper, we observed data that showed that siRNA for DMNT3 in L1 honeybee larvae was sufficient to promote the development of mostly queens. From this observation, I initially inferred that this meant that decreased DNA methylation in L1 larvae would lead to queen development. However, this data alone did not directly show what I had assumed, which made me realize that I could not make conclusions based on assumptions, even if the assumptions seemed logical. In order to validly conclude that decreased DNA methylation is correlated with queen development, we would need to directly measure DNA methylation levels in larvae. Through this experience, I learned that conclusions require direct supporting evidence without having to make assumptions in order to be rigorous.
I think that having a good understanding of the concept of data interpretation is important to ensure that conclusions are valid. Although it is tempting to make extended inferences based on logical assumptions, we need to keep in mind that we can only make conclusions based on the evidence that we have. If assumptions are used to make conclusions, these conclusions may be inaccurate. Therefore, by making precise conclusions that are based only on direct evidence, we can be certain that our conclusions are reasonable.