[pseut]

1. If I want a confidence interval, I want to know the end points of the confidence interval. Trying to guess them from a plot like this is annoying and not helpful.

2. This doesn't work for bar graphs, lengths, etc. The uncertainty is often going to be symmetric around the point estimate, but your opacity forces an asymmetric representation of the uncertainty.

3. Box plots are great. If you want more detail than that, a thin vertical histogram or density is going to convey much more information than shading.

[nhaehnle]

Like all tools you have to aware of its limitations, but I think shading can be a fantastic way to visualize uncertainty.

For example, the Bank of England occasionally uses it in plots of economic forecasts, where time is on the x-axis and things like GDP might be on the y-axis, eg. here http://www.bankofengland.co.uk/publications/Documents/inflat.... The fading out of intensity over time is a great visual reminder that predicting the future is hard.

It is much better when your chart is supposed to be targeted at the general public, because the "smearing out" of the data is very hard to misunderstand, unlike confidence intervals.

[pseut]

From the first few graphs I've seen in the links, the shading is discrete and not continuous (e.g. p 40). Discrete shading does address my first point, and it can help somewhat with communication too. Personally, I prefer simulating from the implicit model and plotting, say, 1000 hypothetical sample paths; but I agree that discrete shading can be effective too.