9 ms·

> It sounds very strange that the death spike is "over" 3 sigma, which should mean over 3 standard deviations, which is really unbelievable, to me this looks li

> It sounds very strange that the death spike is "over" 3 sigma, which should mean over 3 standard deviations, which is really unbelievable, to me this looks like an artefact of some sort Why does the fact that it is larger than 3 standard deviations suggest to you that it must be an artifact? If the death rate is normally very stable then the standard deviation will be small, so it will be easy for any unusual increase to exceed that.

hn_throwaway_99 5y ago
I think the other thing that may be possible (just guessing, I'm not a statistician or actuarial) is that by using the term "3 sigma" I'm assuming they're modeling the data as a normal distribution. But these types of outlier events often follow power laws, such that you get "fat tails" when looking at a bell curve.
- sigstoat 5y ago
  > that by using the term "3 sigma" I'm assuming they're modeling the data as a normal distribution variance and standard deviation don't only apply to normal distributions. and "sigma" is the symbol normally used for variance regardless of the underlying distribution. > I'm assuming sigh.
  - hn_throwaway_99 5y ago
    Yes, I'm fully aware that variance and standard deviation apply to any sample or population, and that sigma is normally used for standard deviation. However, in everyday usage, saying something is a "2 or 3 sigma" event nearly always refers to a normal distribution unless otherwise noted, because otherwise that information doesn't really tell you anything. Is only with a specific distribution that can imply a percentage likelihood, e.g. 5% for a 2 sigma event or .3% for a 3 sigma event. Also, if you're looking at at annual probability, 1-in-200 year event would correspond to just about 3 sigma on a normal distribution. Sigh
    - FabHK 5y ago
      And the number of people dying per (large) unit of time is almost certainly well approximated by a normal distribution, modulo seasonal variation and events such as this (which break the "independence" assumption of the CLT).
    - worik 5y ago
      The point is that you expect a normal distribution without fat tails. Fat tails are a sign of non random processes. Which lets you know some non random process is happening.
      - hn_throwaway_99 5y ago
        As the sibling comment wrote, it's not non-random processes in this instance, it's processes that aren't independent. That is, when generally looking over any death rates in a relatively large population, most deaths in a given year are uncorrelated, so things look like a normal distribution. Obviously with a transmissible virus, the fact that two people died in the same year of Covid is correlated. Similarly, if you did the math from the insurance company's data, I'd bet you'd find the chance of everybody dying in the same year would be like 1 in many, many trillions of years. But of course things like supervolcanos or meteor strikes are possible. Those aren't non-random, it's just that everyone's death would correlate with that single event.

hn_throwaway_99 5y ago

I think the other thing that may be possible (just guessing, I'm not a statistician or actuarial) is that by using the term "3 sigma" I'm assuming they're modeling the data as a normal distribution. But these types of outlier events often follow power laws, such that you get "fat tails" when looking at a bell curve.

sigstoat 5y ago
> that by using the term "3 sigma" I'm assuming they're modeling the data as a normal distribution variance and standard deviation don't only apply to normal distributions. and "sigma" is the symbol normally used for variance regardless of the underlying distribution. > I'm assuming sigh.
- hn_throwaway_99 5y ago
  Yes, I'm fully aware that variance and standard deviation apply to any sample or population, and that sigma is normally used for standard deviation. However, in everyday usage, saying something is a "2 or 3 sigma" event nearly always refers to a normal distribution unless otherwise noted, because otherwise that information doesn't really tell you anything. Is only with a specific distribution that can imply a percentage likelihood, e.g. 5% for a 2 sigma event or .3% for a 3 sigma event. Also, if you're looking at at annual probability, 1-in-200 year event would correspond to just about 3 sigma on a normal distribution. Sigh
  - FabHK 5y ago
    And the number of people dying per (large) unit of time is almost certainly well approximated by a normal distribution, modulo seasonal variation and events such as this (which break the "independence" assumption of the CLT).
  - worik 5y ago
    The point is that you expect a normal distribution without fat tails. Fat tails are a sign of non random processes. Which lets you know some non random process is happening.
    - hn_throwaway_99 5y ago
      As the sibling comment wrote, it's not non-random processes in this instance, it's processes that aren't independent. That is, when generally looking over any death rates in a relatively large population, most deaths in a given year are uncorrelated, so things look like a normal distribution. Obviously with a transmissible virus, the fact that two people died in the same year of Covid is correlated. Similarly, if you did the math from the insurance company's data, I'd bet you'd find the chance of everybody dying in the same year would be like 1 in many, many trillions of years. But of course things like supervolcanos or meteor strikes are possible. Those aren't non-random, it's just that everyone's death would correlate with that single event.