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Forecasts need to have error bars

andrewpwheeler.com

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Re: Forecasts need to have error bars

#41

Post by aftoprokrustes »

amichal wrote: post #5

I have, in my life as a web developer, had multiple "academics" urgently demand that i remove error bands, bars, notes about outliers, confidence intervals etc from graphics at the last minute so people are not "confused" Its depressing

I obviously cannot assess the validity of the requests you got, but as a former researcher turned product developer, I had several times to take the decision _not_ to display confidence intervals in products, and to keep them as an internal feature for quality evaluation.

Why, I hear you ask? Because, for the kind of system of models I use (detailed stochastic simulations of human behavior), there is no good definition of a confidence interval that can be computed in a reasonable amount of computing time. One can design confidence measures that can be computed without too much overhead, but they can be misleading if you do not have a very good understanding of what they represent and do not represent.

To simplify, the error bars I was able to compute were mostly a measure of precision, but I had no way to assess accuracy, which is what most people assume error bars mean. So showing the error bars would have actually given a false sense of quality, which I did not feel confident to give. So not displaying those measures was actually done as a service to the user.

Now, one might make the argument that if we had no way to assess accuracy, the type of models we used was just rubbish and not much more useful than a wild guess... Which is a much wider topic, and there are good arguments for and against this statement.

Re: Forecasts need to have error bars

#42

Post by hgomersall »

bo1024 wrote: post #8

Two things I think are interesting here, one discussed by the author and one not. (1) As mentioned at the bottom, forecasting usually should lead to decisionmaking, and when it gets disconnected, it can be unclear what the value is. It sounds like Rosenfield is trying to use forecasting to give added weight to his statistical conclusions about past data, which I agree sounds suspect. (2) it's not clear what the "erro…

Error bars in forecasts can only mean uncertainty your model has. Without error bars over models, you can say nothing about how good your model is. Even with them, your hypermodel may be inadequate.

Re: Forecasts need to have error bars

#47

Post by niebeendend »

mightybyte wrote: post #35

Completely agree with this idea. And I would add a corollary...date estimates (i.e. deadlines) should also have error bars. After all, a date is a forecast. If a stakeholder asks for a date, they should also specify what kind of error bars they're looking for. A raw date with no estimate of uncertainty is meaningless. And correspondingly, if an engineer is giving a date to some other stakeholder, they should include…

A deadline implies the upper limit of error bar cannot exceed it. That means you need to appropriately buffer to hit the deadline.

Re: Forecasts need to have error bars

#49

Post by macrolocal »

esafak wrote: post #21

Uncertainty quantification is a neglected aspect of data science and especially machine learning. Practitioners do not always have the statistical background, and the ML crowd generally has a "predict first and asks questions later" mindset that precludes such niceties. I always demand error bars.

Also, error bars qua statistics can indicate problems with the underlying data and model, eg. if they're unrealistically narrow, symmetric etc.

Re: Forecasts need to have error bars

#50

Post by m-murphy »

borroka wrote: post #25
m-murphy wrote: post #16

Earlier quoted context omitted.

You're probably thinking of a predictive interval

It is a very common misconception and one of my technical crusades. I keep fighting, but I think I have lost. Not knowing what the "uncertainty interval" represents (is it, loosely speaking, an expectation about a mean/true value or about the distribution of unobserved values?) could be even more dangerous, in theory, than using no uncertainty interval at all. I say in theory because, in my experience in the tech ind…

Agreed! I also think it's extremely important as practitioners to know what we're even trying to estimate. Expected value (i.e. least squares regression) is the usual first thing to go for, does that even matter? We're probably actually interested in something like an upper quantile for planning purposes. And then the whole model component of it, the interval that's being simultaneously estimated is model driven and if that's wrong, then the interval is meaningless. There's a lot of space for super interesting and impactful work in this area IMO, once you (the practitioner) think more critically about the objective. And then don't even get me started on interventions and causal inference...
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