Effective Altruism is largely about optimizing expected value. Expected value estimates often have high uncertainty for multiple inputs, but the uncertainty of the outputs is rarely calculated.
Doing math with probability distributions is possible using Monte Carlo simulations, but few people do this. One reason is that the tools have been relatively inaccessible. The options consist mostly of expensive Excel plugins and statistical programming packages, all of which have significant learning curves and mediocre sharing abilities.
After a lot of late nights, I now believe Guesstimate works quite well. I’ve used it to to figure out when to leave for meetings, to better understand risks of common activities, to make all kinds of decisions, and to replicate some of the standard EA models (Like 80,000 Hours’ estimate of the influence of being a UK politician).
Guesstimate is similar to Excel in that it uses a spreadsheet format. However, while Excel is general purpose and good at analyzing existing data, Guesstimate is specifically for making models of uncertain estimates. This means a few things:
Every cell can be a probability distribution instead of a number. Ranges are denoted as [5th percentile, 95th percentile]. 
Every cell represents a metric, with both a name and value. Descriptions can be added to each metric separately. 
Models are meant to be shared online publicly. 
I don’t know what Guesstimate is ultimately going to become. I have a ton of ideas about where I would like it to go, but I’m going to first follow what people ask for. I encourage you to try estimating interesting and useful things. I'm excited to see what people will come up with.
 Right now it simulates around 5000 samples per calculation. It does this even for simple calculations, like addition for normal distributions. Over time we could do some of these analytically, but for now this seemed like the simpler option.
 In the future, obviously comments, versioning, and unique URLs could be part of this.
 This means both that it’s easy to share them, but also that in the future we could add interactivity between public models. For instance, if one person estimates the ‘expected earnings of investing in a high risk ETF’, someone else could use that in their model of the opportunity cost of exercising stock options. It also means that the focus is more on the reader than the writer.