Tips and Tricks on How to Interpret Data Like a Pro
Society is inundated with an obscene amount of data, which makes it difficult for individuals to know which sources to trust. To complicate matters more, park and recreation practitioners oftentimes need quickly accessible data and research to inform their programs and services. However, it can be extremely daunting and stressful for practitioners to not only identify resources with useful data but to also know how to interpret the data accurately. This blog post will help address that need by providing a couple of tips and tricks to help park and recreation practitioners interpret data like a professional.
Now, before we begin, I must make a disclaimer. The couple tips and tricks that I outline in this blog post are not exhaustive. You will not become a data analysis expert after reading this post, but you will hopefully learn a couple strategies that professional data analysts employ in their daily practices.
To begin, numbers themselves cannot lie, but the people that use them can. What I mean is that individuals can create charts and graphics in a way to make you believe something that is not reflective of reality. This is not a common practice overall, but it does occur enough so that people should be made aware of it. One example of creating misleading graphics is when individuals graph two completely unrelated trends on the same graph to give the illusion of a correlation.
Correlation is a fancy term for relationship, and in the data analysis field, correlations are created to assess how strong (or weak) the relationship is between two things. For example, there is a strong correlation between weight and the number of calories you eat. The more calories you eat, the more likely you are to gain weight and vice versa. However, people need to remember that many other variables can be at play. Weight gain is not attributed only to caloric intake but can also be moderated by age, gender, genetics, and a slew of other variables. This is why park and recreation practitioners should be mindful whenever they come across correlational data. The correlation does not give any insight into causality but is simply a measure of how strong the two things are related. ‘Correlation does not imply causation’ is a saying that data analysts use to remind ourselves to not get too excited when we come across a strong correlation for this exact reason.
For context, a great deal of studies that analyze the health benefits of exposure to nature are correlational. Research still has a long way to go before we understand exactly how nature may be beneficial for human health. The evidence of nature’s efficacy with improving health is most certainly growing, but we should still exercise caution with how far we take these claims.
Please visit the links provided in the references section to learn more about how to interpret data efficiently and wisely. The resources are short, sweet, and to the point!
Esteban, C. (2015, June 19). A Quick Guide to Spotting Graphics That Lie. National Geographic. Retrieved from https://www.nationalgeographic.com/news/2015/06/150619-data-points-five-ways-to-lie-with-charts/
McCluney, K. (2011, July 16). Finding good information on the internet. Scientific American. Retrieved from https://blogs.scientificamerican.com/guest-blog/finding-good-information-on-the-internet/
TEDx Talks. (2016, November 3). How to defend yourself against misleading statistics in the news | Sanne Blauw | TEDxMaastricht. [Video file]. Retrieved from https://www.youtube.com/watch?v=mJ63-bQc9Xg
TED-Ed. (2017, July 6). How to spot a misleading graph – Lea Gaslowitz. [Video file]. Retrieved from https://www.youtube.com/watch?v=E91bGT9BjYk