The 2016 presidential election brought attention to the limitations of Statistics. Most models predicted a Clinton win but Trump will most likely be the president (the results are currently unofficial and recounts are in progress but most experts believe that Trump will be officially elected president). However all models are not 100% certain and the goal of statistics is to find the most likely event. I have spent the last few weeks reflecting on the results and what this means for the field of political science statistics. Recently I read a book by David Salsburg called: The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century. It’s a history of sorts of how the field was developed and then applied to science. While an exact date of the beginning of statistics is hard to pinpoint the first journals and departments were founded in the early twentieth century. Statistics is a young field and is constantly growing and evolving as more data and situations are studied. In the beginning some of the problems may have been trivial, but it is important to try to understand the world around us. Collecting data from an entire population is incredibly difficult and sometimes impossible, so methods of estimation were created. You may wonder why prediction is necessary or helpful. After all eventually the election happens and the president is chosen, so why do we care about knowing this in advance? Why does prediction matter? Statistics models and research is not just about what is being studied but about creating better ways to understand the world around us. We can begin to better understand things like the opinions of the people, development of diseases, and the economy. Statistics can create better government, better medicine, and better education, and a better world. If we can understand how polls measure the voting habits of the American people, then we may be able to get a better picture of citizens views on multiple issues and candidates. If we can help understand how diseases like cancer behave, then we can create better more individualized medicine. If we can understand how individual students learn and what they know, then we can create a better educational system. Statistics isn’t perfect. Statisticians can disagree and still both have valid models and reasoning. The data may be imperfect and incomplete. The model may be wrong. The experiment may seem trivial and unimportant. But there is so much potential for the field of Statistics to change our world. Just because prominent statisticians like Nate Silver may not have seen a Trump presidency as the most likely event doesn’t mean that the field should be discounted.
Models May Fail but Statistics Matters Anyway by BrittanyAlexander is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International