The headline might be a bit strong, but I have been seeing comments about the statistics of the COVID–19 virus both by people chatting online and commentators on TV that are not correct. Sometimes statistics can seem counterintuitive. What seems to make sense truly doesn’t. Let me give an example from my life. I was in my very early 20s when I went to Vietnam. Statistically, my chances of dying went up when I entered Vietnam. I had been in Vietnam about a month when six of the men I trained with had been killed. Statistically, my group had what would be considered an average month at that time in terms of the number of deaths for the whole of Vietnam. Had my chances of dying statistically gone up or down?
Before you answer that question, it is one of the things I see in chats that is wrong. People seem to think if the statistical average has been reached, it means they are less likely to get sick or die. Statistical averages are not goals. They may or may not influence your chances of getting the virus or dying of it. Broadly speaking meeting, the statistical average does not change your chance of dying. If the number of deaths exceeds the statistical average by a wide margin, your chance of dying has actually gone up.
Back to Vietnam – the deaths of the men I had trained with happened before the big buildup started. It was an unusual occurrence. It did not happen in the next month or the month before. It meant my chances of dying in Vietnam essentially remained unchanged. It was a fluke. Once the big buildup started, larger numbers of men dying produced a different statistical average. It didn’t change my statistical chances of dying because I was not doing what the main force troops in the buildup were doing. That’s similar to seeing the statistics for New York City and applying that probability to a remote Kansas farm. The statistics for New York City do not apply to that farm.
I am sure if there is a mathematician reading this blog, they will probably disagree – as I said, statistics can be very counterintuitive. However, I just wanted to warn people not to apply statistical averages they see on TV or read about to themselves. There are too many factors influencing those figures you may not know.
