Figure 1: this diagram illustrates a hierarchy of source credibility, with the more accurate sources on the bottom and the least accurate souces on the top. Image via Illinois State University Library.
In a world where we have the power to share whatever pops into our heads with billions of other individuals instantaneously, it is understandable that so many falsities and innacuracies are perpetuated. The only thing that can contradict and/or obstruct a given opinion or piece of “research” is the skepticism of others. This is both a weakness and a strength in our learning communities, the former in that the situation is often a “your word against mine” spitting match, and the latter in that productive collaboration and corroboration often produce superior results.
Figure 2: format can be misleading! Just because something is cited “officially” doesn’t make it credible. Image by XKCD Comic via the PSU Methodology Center on Twitter.
Kessler’s article describes these issues well. Bias is a shackle on true scientific advancement, and the delivery of information in general. Many researchers try their best to be objective, but others shamelessly publish “findings” that are the result of professional data cherry picking––these individuals could be sponsored by organizations with agendas, or they could be acting on personal confirmation bias. However, whichever camp a given scientist falls into, they are prone to bias whether they like it or not. Kessler’s citation of Gladwell acknowledges this.
Thus, it is of paramount importance to consider what the objectives of any given researcher are, and who funds them. A “study” proclaiming beef to be extremely beneficial to one’s health that was funded by the food industry is likely a questionable source. Research published by nonprofit organizations is more likely to be trustworthy than that published by for-profit ones. Findings that span reports are more likely to be accurate than a single case study. These are the considerations that make or break the background research that bolsters a claim or perspective.
I thought it was interesting how you introduced the term data cherry picking. It perfectly describes what the Kessler’s article is getting at. I also agree that we must examine the funding behind research, because it is likely to result in bias. As a side note, the reading the graphics in your blog is always fun-keep up the good work!