MIT formula for uncertainty: pad your estimates

I don’t have the time to delve into the entire article, so I may be misrepresenting the thrust of Spyros Makridakis, Robin M. Hogarth and Anil Gaba’s Winter 2010 article in the MIT Sloan Management Review (full article here).   Or more properly, I hope that this Forbes excerpt — Why Forecasts Fail. What To Do Instead — misrepresents the piece.  For instance, what do the authors mean here?

Say you’re a publisher and have an unknown author selling her first novel. Publishers should look at the sales track records of first-time novelists in general. The uncertainty surrounding your author shouldn’t differ from the wider population of new authors. You should be able, therefore, to estimate how low or high the sales might go. That range probably covers 95% of all possible outcomes. The next step would be to take the estimated range and increase it. 

Wow…I guess the WAG now has the MIT Sloan seal of approval.  Or perhaps it is CYA that’s now approved.  Other than allowing a forecaster to say — “I told you so” —  is there really all that much use in simply increasing the range of estimates? 

Maybe we should spend our time learning how to distinguish the outliers quickly — e.g., from distinct initial sales patterns or unusually intense media coverage — so we can take appropriate action?

ROM Estimation training website

Rough order-of-magnitude (ROM) estimates are a long-standing way to get a feel for how big your project or program will be.  Nothing too precise of course, but that’s to be expected. 

Speaking for myself, I haven’t paid too much attention to practicing this technique.  To remedy that, I’ve been doing some visual training that you all might find useful as you prep for your next wild-ass guess ROM.  

See how good you are at “eyeballing” here.

%d bloggers like this: