Learning from Small Numbers

I am not a big fan of inferential statistics. In the USA, where I live, I believe that the widespread reporting of survey results is the second greatest contributor to the deterioration of democracy (the infusion of obscene amounts of money into the political process being #1). People hear or read that candidate A, or ballot question Y, has a double-digit lead in opinion polls, and little by little they begin to disengage from the process. The inferential statistic is treated like a big data pronouncement of a political inevitability.

There are times, however, when small bits of data representing a full data set, can serve as reminders that you need to check your own assumptions on a periodic basis. Take this website for example. My experience in writing about the concepts and ideas explored here is limited to my perspective as an American. This site’s analytics regarding the country of origin of its visitors, provide a good case for small, but complete sets of numbers.

In 2015, non-U.S. readers compromised 30% of the visitors to this site. So far in 2016, 59% of my readers have been from outside the United States of America. The U.K, Romania, and Brazil, are obviously very different places. They are also three countries with a high representation in my readership. Unless all of these readers are simply interested in analyzing leadership, and change agentry in the U.S., my paying attention to the universality of the strategies that I recommend is probably a good idea.

I am well aware that leading social change is tied intimately to the realities of local culture, and to place-based historical context. If you are a non-U.S. reader of these pages, please feel welcome to share your realities, along with your wisdom, and your questions, in the comments of this blog.

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