4 ms·

Which is 100% misleading, and strips all accountability from the predictor. Show us your prediction and the difference between the actual and predicted results

Which is 100% misleading, and strips all accountability from the predictor. Show us your prediction and the difference between the actual and predicted results to see how well you did. Stop moving the goalposts too. Make the prediction for all days in the future once, and don't change it based on the current numbers.

dredmorbius 7y ago
https://news.ycombinator.com/item?id=22185174 https://news.ycombinator.com/item?id=22185174 Though near-term projection based on current trend is useful.
- mlyle 7y ago
  Eventually more humans will die from it than have ever been born! ;)
  - dredmorbius 7y ago
    It's the Four Yorkshiremen Epidemic.
mlyle 7y ago
It's just an exponential fit based on the current number. You can run that same fit for earlier days and eyeball the graph, but we have a fancier tool for that called a "correlation coefficient". > Stop moving the goalposts too. Make the prediction for all days in the future once, and don't change it based on the current numbers. So, don't ever improve the prediction based on new data. Mkay. Extrapolating out into the future is always errorprone. Exponents don't ever continue forever and they look like great models until they don't.
- AznHisoka 7y ago
  You can definitely improve your prediction based on new data, but the most important part is to show your historical predictions too. FiveThirtyEight does this pretty well by showing the probability a candidate gets elected in a time series chart. Predicting is serious business, and you have to be accountable for past predictions, especially if they were wrong. This chart, as is is extremely misleading because it gives readers the false sense that you were 100% right, and your future predictions are highly likely to be true. >> Extrapolating out into the future is always errorprone. You're absolutely right. And if readers saw your predictions were mostly wrong, they wouldn't be alarmed in the future when they see "experts" predict 10% of the world would die 3 months after a virus is found. Because they'd know from past experience that past models were mostly wrong!
  - mlyle 7y ago
    > This chart, as is is extremely misleading because it gives readers the false sense that you were 100% right, and your future predictions are highly likely to be true. Instead, if readers saw your past predictions were mostly wrong, they wouldn't have this false perception. Just hit the button to toggle to the log scale version of it. A exponential fit is appropriate and past predictions using an exponential fit would be good. :P Do you understand how to fit data series? My fifth grader could hold up a ruler-- align it the the beginning of the yellow line and each subsequent point and see how reasonable of a prediction they are of future points. He'd say "Yup, it's pretty straight!" And even for the red line, though a little less so. Yah, it would be cool if they'd tell us r^2. > And if readers saw your predictions were mostly wrong, they wouldn't be alarmed in the future when they see "experts" predict 10% of the world would die 3 months after a virus is found. Note that exponential models predict everyone will get the disease and everyone will die if you extrapolate out far enough. (Actually, -more- than everyone heh). Obviously we don't think that's the case. But it's a pretty damn reasonable way to see 3 days forward in the early phases of an epidemic.
    - vackosar 7y ago
      agreed. I mention there that this is simplistic. I may look into the sigmoid shape s-curve once it is time for it. If the site is still interesting to somebody at that time.

dredmorbius 7y ago

https://news.ycombinator.com/item?id=22185174 https://news.ycombinator.com/item?id=22185174 Though near-term projection based on current trend is useful.

mlyle 7y ago
Eventually more humans will die from it than have ever been born! ;)
- dredmorbius 7y ago
  It's the Four Yorkshiremen Epidemic.

mlyle 7y ago

It's just an exponential fit based on the current number. You can run that same fit for earlier days and eyeball the graph, but we have a fancier tool for that called a "correlation coefficient". > Stop moving the goalposts too. Make the prediction for all days in the future once, and don't change it based on the current numbers. So, don't ever improve the prediction based on new data. Mkay. Extrapolating out into the future is always errorprone. Exponents don't ever continue forever and they look like great models until they don't.

AznHisoka 7y ago
You can definitely improve your prediction based on new data, but the most important part is to show your historical predictions too. FiveThirtyEight does this pretty well by showing the probability a candidate gets elected in a time series chart. Predicting is serious business, and you have to be accountable for past predictions, especially if they were wrong. This chart, as is is extremely misleading because it gives readers the false sense that you were 100% right, and your future predictions are highly likely to be true. >> Extrapolating out into the future is always errorprone. You're absolutely right. And if readers saw your predictions were mostly wrong, they wouldn't be alarmed in the future when they see "experts" predict 10% of the world would die 3 months after a virus is found. Because they'd know from past experience that past models were mostly wrong!
- mlyle 7y ago
  > This chart, as is is extremely misleading because it gives readers the false sense that you were 100% right, and your future predictions are highly likely to be true. Instead, if readers saw your past predictions were mostly wrong, they wouldn't have this false perception. Just hit the button to toggle to the log scale version of it. A exponential fit is appropriate and past predictions using an exponential fit would be good. :P Do you understand how to fit data series? My fifth grader could hold up a ruler-- align it the the beginning of the yellow line and each subsequent point and see how reasonable of a prediction they are of future points. He'd say "Yup, it's pretty straight!" And even for the red line, though a little less so. Yah, it would be cool if they'd tell us r^2. > And if readers saw your predictions were mostly wrong, they wouldn't be alarmed in the future when they see "experts" predict 10% of the world would die 3 months after a virus is found. Note that exponential models predict everyone will get the disease and everyone will die if you extrapolate out far enough. (Actually, -more- than everyone heh). Obviously we don't think that's the case. But it's a pretty damn reasonable way to see 3 days forward in the early phases of an epidemic.
  - vackosar 7y ago
    agreed. I mention there that this is simplistic. I may look into the sigmoid shape s-curve once it is time for it. If the site is still interesting to somebody at that time.