Editing Talk:2110: Error Bars

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I put in a little thing about fractals and Cantor sets, seemed relevant. [[User:Netherin5|Netherin5]] ([[User talk:Netherin5|talk]]) 17:32, 11 February 2019 (UTC)
: Fractals seem more relevant than the Cantor set, since the Cantor set is the limit as you infinitely subtract stuff from a finite interval, whereas this is adding (more like a geometric series).[[Special:Contributions/172.68.174.64|172.68.174.64]] 23:25, 12 February 2019 (UTC)

Would the series have a limit or would it continue on until the error bars go from infinity to +infinity?
      It will have limit. Becouse it will every time be 5% of prevous errors it will lower over time.   --172.68.154.40 
             https://repl.it/repls/AppropriateMatureConferences for demonstration  --172.68.154.40
: Yes and no. The point is in fact that for calculating one sigma you need at least two points. It means that sigmas is always one less then points. And sigma on sigmas is less in mumber by one more. So you can't have recursion sigmas depth more then number of point minus one (you have to start with two). Comics is wrong in this one: it has four points and four levels (on graph), but it can have only 3.

Perhaps you should review [https://www.explainxkcd.com/wiki/index.php/1153:_Proof Zeno's paradoxes] [[User:PotatoGod|PotatoGod]] ([[User talk:PotatoGod|talk]]) 02:11, 12 February 2019 (UTC)


That's not my understanding of "propagating error". I understand that phrase to mean that you're taking a measured value (that has uncertainty) and plugging it into a formula / using it calculate another value. Because of the way this works, the (absolute & relative) error on the newly calculated value is likely to be larger or smaller than the error in the original value (the overall size of the error bars changes). Randall's joke is that, instead of calculating the new error bars, he calculates error bars on the ends of his existing bars. I also agree with Netherin5 that there's a clear fractal reference here.
[[Special:Contributions/162.158.79.113|162.158.79.113]] 17:45, 11 February 2019 (UTC) hagmanti
: I noticed this error too, and tried to correct it, but it sounds like you understand it better than I.  Feel free to edit the article itself! [[Special:Contributions/162.158.78.178|162.158.78.178]] 23:38, 11 February 2019 (UTC)

A question too, does the CI tend towards negative infinity, zero, or one? Edit: Today I learned what CI means. [[User:Netherin5|Netherin5]] ([[User talk:Netherin5|talk]]) 18:01, 11 February 2019 (UTC)

He has confidence intervals in confidence intervals alone; despite this, you see, he lacks confidence in...he. [[User:GreatWyrmGold|GreatWyrmGold]] ([[User talk:GreatWyrmGold|talk]]) 20:48, 11 February 2019 (UTC)

Just to pair with the alt-text: ...))))))))))).  [[Special:Contributions/108.162.242.19|108.162.242.19]] 02:31, 13 February 2019 (UTC)
    https://explainxkcd.com/859/ relevant --172.68.154.64

Good explanations but if I understand the comic correctly, the article does not really get to the point. It is indeed true that different modelling assumptions will give different confidence intervals, but a more mundane and more important source of uncertainty is statistical error (e.g., sampling error). CIs are typically used to convey the uncertainty around a point estimate (e.g., a mean) which has been computed from a random sample. If you take another random sample from the same population (e.g., perform an exact replication of an experiment), you will get a different mean, but also a different CI. See Cumming's dance of p-values and CIs for an illustration: https://www.youtube.com/watch?v=5OL1RqHrZQ8, or a talk I gave that covers a larger range of statistics: https://www.youtube.com/watch?v=UKX9iN0p5_A. In my talk I explain why it doesn't make sense to report inferential statistics (p-values, CIs, etc) with many significant digits, because you could have easily obtained very different p-values or CIs. The belief that inferential statistics are "stable" across replications is a very common misconception that can easily lead of erroneous inferences. So if you care about your statistical analyses being interpreted correctly, it is tempting to show the uncertainty around all the inferential statistics you report, including CI limits, as Monroe is suggesting. Like any statistics, CI limits are a function of the data and thus have a sampling distribution (https://statmodeling.stat.columbia.edu/2016/08/05/the-p-value-is-a-random-variable/). Thus you can estimate the standard deviation of this sampling distribution, and this gives you the standard error of the confidence limit. There is one inaccuracy in the comic (I think): you can't define CIs on CI limits, because there is no true population value of CI limits. However you can compute standard errors of CI limits, or alternatively prediction intervals, and then compute standard errors and prediction intervals again and again, recursively. If my explanation makes any sense I can try to summarize it and incorporate it in the article. [[User:Dragice|Dragice]] ([[User talk:Dragice|talk]]) 10:47, 13 February 2019 (UTC)
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	Good explanations but if I understand the comic correctly, the article does not really get to the point. It is indeed true that different modelling assumptions will give different confidence intervals, but a more mundane and more important source of uncertainty is statistical error (e.g., sampling error). CIs are typically used to convey the uncertainty around a point estimate (e.g., a mean) which has been computed from a random sample. If you take another random sample from the same population (e.g., perform an exact replication of an experiment), you will get a different mean, but also a different CI. See Cumming's dance of p-values and CIs for an illustration: https://www.youtube.com/watch?v=5OL1RqHrZQ8, or a talk I gave that covers a larger range of statistics: https://www.youtube.com/watch?v=UKX9iN0p5_A. In my talk I explain why it doesn't make sense to report inferential statistics (p-values, CIs, etc) with many significant digits, because you could have easily obtained very different p-values or CIs. The belief that inferential statistics are "stable" across replications is a very common misconception that can easily lead of erroneous inferences. So if you care about your statistical analyses being interpreted correctly, it is tempting to show the uncertainty around all the inferential statistics you report, including CI limits, as Monroe is suggesting. Like any statistics, CI limits are a function of the data and thus have a sampling distribution (https://statmodeling.stat.columbia.edu/2016/08/05/the-p-value-is-a-random-variable/). Thus you can estimate the standard deviation of this sampling distribution, and this gives you the standard error of the confidence limit. There is one inaccuracy in the comic (I think): you can't define CIs on CI limits, because there is no true population value of CI limits. However you can compute standard errors of CI limits, or alternatively prediction intervals, and then compute standard errors and prediction intervals again and again, recursively. If my explanation makes any sense I can try to summarize it and incorporate it in the article. [[User:Dragice\|Dragice]] ([[User talk:Dragice\|talk]]) 10:47, 13 February 2019 (UTC)		Good explanations but if I understand the comic correctly, the article does not really get to the point. It is indeed true that different modelling assumptions will give different confidence intervals, but a more mundane and more important source of uncertainty is statistical error (e.g., sampling error). CIs are typically used to convey the uncertainty around a point estimate (e.g., a mean) which has been computed from a random sample. If you take another random sample from the same population (e.g., perform an exact replication of an experiment), you will get a different mean, but also a different CI. See Cumming's dance of p-values and CIs for an illustration: https://www.youtube.com/watch?v=5OL1RqHrZQ8, or a talk I gave that covers a larger range of statistics: https://www.youtube.com/watch?v=UKX9iN0p5_A. In my talk I explain why it doesn't make sense to report inferential statistics (p-values, CIs, etc) with many significant digits, because you could have easily obtained very different p-values or CIs. The belief that inferential statistics are "stable" across replications is a very common misconception that can easily lead of erroneous inferences. So if you care about your statistical analyses being interpreted correctly, it is tempting to show the uncertainty around all the inferential statistics you report, including CI limits, as Monroe is suggesting. Like any statistics, CI limits are a function of the data and thus have a sampling distribution (https://statmodeling.stat.columbia.edu/2016/08/05/the-p-value-is-a-random-variable/). Thus you can estimate the standard deviation of this sampling distribution, and this gives you the standard error of the confidence limit. There is one inaccuracy in the comic (I think): you can't define CIs on CI limits, because there is no true population value of CI limits. However you can compute standard errors of CI limits, or alternatively prediction intervals, and then compute standard errors and prediction intervals again and again, recursively. If my explanation makes any sense I can try to summarize it and incorporate it in the article. [[User:Dragice\|Dragice]] ([[User talk:Dragice\|talk]]) 10:47, 13 February 2019 (UTC)
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−	I doubt this was intended but what was brought to my mind -- and then what I couldn't get out of my head until I created one specifically for this comic -- was this old meme: [https://imgflip.com/i/3bffam Yo Dawg, I heard you like error bars...]. Thought it worth contributing! :) [[Special:Contributions/172.68.47.114\|172.68.47.114]] 16:56, 24 September 2019 (UTC)Brian