{"id":27850,"date":"2021-10-28T21:17:44","date_gmt":"2021-10-29T04:17:44","guid":{"rendered":"https:\/\/blogs.georgefox.edu\/dlgp\/?p=27850"},"modified":"2021-10-28T21:24:57","modified_gmt":"2021-10-29T04:24:57","slug":"27850-2","status":"publish","type":"post","link":"https:\/\/blogs.georgefox.edu\/dlgp\/27850-2\/","title":{"rendered":"Statistics Is Science"},"content":{"rendered":"

The Chivers brothers, Tom and David, authored How to Read Numbers,\u00a0<\/em>a book written with a mathematical\/scientific bent to aid in the proper and accurate delivery of information in media. Both authors provide a unique perspective. Tom, a journalist with a passion for maintaining scientific integrity within journalism, and David, a mathematician serving as a professor in economics. Their conviction is that we should care about numbers as they undoubtedly make a difference in the world. They write, \u201cwe’re going to talk a lot about numbers; about how they’re used in the media, and about how they can go wrong – and give misleading impressions. But along the way we will need to remind ourselves that those numbers stand for something. Often they will represent people, or if not people, then things that matter to people.”[1]<\/a> In short, they argue that we must approach, use, and understand statistics accurately to make good decisions; failure to do so will result in poor choices that are detrimental to both individuals and society alike.<\/p>\n

Within the body of the book, several important concepts were of particular interest to me. I will highlight two. One was the Simpson’s Paradox, <\/strong>the problem of using the same data that can tell two opposing stories. Regarding my work among vulnerable populations, I have witnessed the use of data toward this end. While the argument is factual, it is only partially valid in that the same data can also be used to communicate a very different and contrary message. Going forward, mindfulness of this paradox will challenge me to pause and consider how I am using, and hopefully not manipulating, data to provide a compelling case for my argument.<\/p>\n

The second concept was determining what constitutes a big number<\/em><\/strong>. While our cohort was in DC, my wife and I walked through the Covid Memorial in the Washington Mall. The day we visited, there had been 676,286 deaths in the US as a direct complication from Covid. Tragic, no doubt. This was especially impactful for my wife, who works as an internal medicine doctor in the hospital; thus, she has taken care of and witnessed the deaths of many Covid patients. Emotions aside, I ask, is this a large number?<\/em> According to Wikipedia, there were 405,399 US deaths during World War II from 1941-1945.[2]<\/a> Comparing these two numbers, it does seem that the number of Covid deaths is high. Yet, as we consider the number of deaths in the US in 2019, there were 2,854,838 deaths.[3]<\/a> Again, we must ask, is this a big number?<\/em> At the same time, a recent article gives evidence that there was a significant rise in the death rate in the US in 2020, 15-19% in fact, with strong evidence of a direct correlation to Covid.[4]<\/a> In the world of statistics, the point is that context is everything to best understand and analyze data. To aid in understanding the context, the size of the denominator is critical in determining whether or not a number is big as it provides a framework for comparison and analysis. Concerning this challenging conundrum, the authors write, “When is a number a big number? There’s no such thing, really.”[5]<\/a><\/p>\n

These particular guide points from the authors will go with me as I consider my NPO:<\/p>\n