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| node: | The 2019-nCoV Statistics Are Misleading: Why Everyone Already Knows And Acts Like This |
| template: | 4 |
| parent: | Covid-19 / Novel Coronavirus (2019-nCoV) |
| owner: | toxygen |
| viewed by: | |
| created: | 29.01.2020 - 14:20:48 |
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| system: | public |
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https://towardsdatascience.com/why-everyone-knows-and-acts-like-the-2019-ncov-statistics-are-misleading-5919b3c33476
The Wuhan coronavirus (2019-nCoV) is currently spreading throughout China and the world. As of Jan 25, 12am EST, there are 1,354 confirmed cases and 41 confirmed deaths throughout the world. While there are questions about whether these statistics are true or not, people are not appreciating the fact that these statistics are not like the other statistics they encounter. These statistics belong to a completely different class of statistics. This ends up having major consequences from a statistical and a practical perspective.
...
When people say that the official figure is an underestimate, they forget this: the official figure isn’t an estimate for the true number of cases. The official figure isn’t even trying to estimate the true number of cases, because it will be wrong anyway. It only reports the number of confirmed cases. The number of suspected and quarantined cases are also reported — these figures are the ones to focus on, to get a better idea of the situation. And even though these figures could still be misleading, it seems like people are not necessarily misled by these figures. People act fearfully and take precautions that seem unnecessary, but they know intuitively that the reported numbers are not the true numbers. This is why face masks are selling out in China right now — they know the true figures are worse and they are preparing for it.
...
In general, if the figures are not worse, then they may eventually be. This is why you should be careful of people who tell us to be “rational” and “stick to the data” — something that is very common among high profile individuals. Steven Pinker and Bill Gates both did it. They talk about the overwhelming inappropriate negative focus of the media, and they are both wrong. I wrote about how to better understand data and uncertainty — by looking at the generating process (the underlying probability distribution), instead of the final outcome (statistics).
The Wuhan coronavirus (2019-nCoV) is currently spreading throughout China and the world. As of Jan 25, 12am EST, there are 1,354 confirmed cases and 41 confirmed deaths throughout the world. While there are questions about whether these statistics are true or not, people are not appreciating the fact that these statistics are not like the other statistics they encounter. These statistics belong to a completely different class of statistics. This ends up having major consequences from a statistical and a practical perspective.
...
When people say that the official figure is an underestimate, they forget this: the official figure isn’t an estimate for the true number of cases. The official figure isn’t even trying to estimate the true number of cases, because it will be wrong anyway. It only reports the number of confirmed cases. The number of suspected and quarantined cases are also reported — these figures are the ones to focus on, to get a better idea of the situation. And even though these figures could still be misleading, it seems like people are not necessarily misled by these figures. People act fearfully and take precautions that seem unnecessary, but they know intuitively that the reported numbers are not the true numbers. This is why face masks are selling out in China right now — they know the true figures are worse and they are preparing for it.
...
In general, if the figures are not worse, then they may eventually be. This is why you should be careful of people who tell us to be “rational” and “stick to the data” — something that is very common among high profile individuals. Steven Pinker and Bill Gates both did it. They talk about the overwhelming inappropriate negative focus of the media, and they are both wrong. I wrote about how to better understand data and uncertainty — by looking at the generating process (the underlying probability distribution), instead of the final outcome (statistics).