Do We Put Too Much Stock in Statistics?

The average business student hears some derivative of the term ‘entrepreneurship’ at least twenty times a day. In fact, we consider ourselves lucky if we do not hear it twenty times in a single lecture.

We recently had a brilliant guest speaker attend our management class and talk about the path that his life took while he developed as an entrepreneur.

Each entrepreneur who comes to deliver a session has their own interesting story about the highs and lows of ‘going it alone,’ and ‘betting on myself’. That said, the vast majority of speakers start with the same statistic: an average of eighty people in London apply to every one job.

This week’s speaker added a new statistic to our collection. Statistically, the oldest sibling is most likely to become an entrepreneur. As the oldest of three, that’s a great ego-boost. To my colleague, the oldest of five, even more so.

However, we often do not consider what statistics really are: patterns of past occurrences.

The mere fact that multiple older siblings before me have become successful entrepreneurs has no impact on whether I will do the same. It makes no difference whether this statistic is taken from a sample of a hundred or a hundred thousand. If, however, statistically, older siblings had a stronger disposition to the skills required to become a successful entrepreneur… well, now we are talking!

Essentially, any business student will tell you that one of the first things drummed into us at university is the importance of using secondary research statistics in our academic writing. We are not taught, however, the importance of not only using statistics, but using important statistics. Even more important, the loopholes in statistics.

The question involved in all of this is ‘do we put too much stock in statistics?’ If I’m the older sibling gaining the ego boost from a statistic working in my favour, great. But for every older sibling encouraged, there is at least one younger sibling who may be disappointed by that one statistic.

Ultimately, statistics is the process of projecting forward from historic trends, not a guaranteed prediction. Therefore, next time we are told that there are seventy-nine other people out for the same job as us, please consider the fine-print. Encourage us to question these statistics, not just accept them blindly, and allow others’ pasts to dictate our futures.

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