Prompt: Explain the significance that each of these questions has on interpreting scientific literature. Which is most helpful when discussing the controversy, and why?
- Can experiments detect differences that matter? – Scientific research is so complex so therre could be differences that are not being detected due to lack of knowledge or technology. Other times differences could be detected because of an oversimplification of data.
- Does the study show causation or correlation? – Data can be interpreted wrongly due to reverse causality. There can be things that we think cause a disease when maybe the disease is what is causing it. Othertimes there are confounding variables that might be causing the correlation between the variables being studied. In other cases the data showing a correlation might just be a coincidence. It is important for scientific research to point out correlation does not equal causation.
- What is the mechanism? – Once data shows there is correlation or causation it is important to find out why. This is where the mechanism comes into play to find the exactly how something causes something else and create a better overall understanding.
- How much do experiments reflect reality? – In experiments settings are closely controlled, this is not reality. Therefore, would these changes or things occur the same with all the other factors that happen in environments outside the lab. Also research is often done on animal models in hopes that it can describe a human mechanism but of course we are different from other animals so the findings might not able to be generalized.
- Could anything else explain the results? – This is similar to the confounding variable topic I discussed previously. There could be other factors that create results in an experiement and sense they might not be what researchers are analyzing they could unintentionally be overlooked. It is important to use solid methods and thouroughly research the topic before making claims that might not be true.
I think the most important question is “could anything else explain the results?” I feel like this is often where the misinterpretation of science and the beginning of conspiracy theories can stem from. Researchers need to look at the full picture and need to be held to the standards of critical analyses of data.
