3 ms·

> 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 d

> 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

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.