I more or less agree with your specific points, but in the larger scheme of things I'm more concerned that these hypothetical people don't understand the stats than that the tool won't scale or is badly implemented.
That said, it sure is a bitch of a language to try to develop for (since the consensus seems to be: write everything in C) and cran is a ghetto.
Unless I skimmed over part of the post, tarsnap is still run by crontab on a unix/linux box. So, still pretty geeky. And the idea that "affordable encrypted backup" can't be understood by "a suit" is silly.
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.
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.
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.
I can't find the quote now, but I'm sure that I've read of someone saying (when told that a result had been found earlier but forgotten), "Yes, but when I discovered it, it stayed discovered!"
A priori, sure. After the fact, if you have detailed enough data it can be pretty easy to identify the timing of certain events.
It doesn't mean that the reaction is correct or that it won't be undone in the future. But there's a huge difference between "day to day fluctuations are unpredictable" and "day to day fluctuations are inexplicable."
Twitter was also down 7% today do you think that's explained by the Occulus deal?
The reality is the markets react to thousands of individual factors and often small changes lead into positive feedback loops. So, while the timing may be explained by some news breaking the magnitude of change is generally influenced by all those other factors.
