By David Shaywitz, MD

My twitter stream is awash in math this morning, cheering Nate Silver’s exceptional forecasting (“Triumph of the Nerds: Nate Silver Wins In Fifty States”, Chris Taylor wrote), and celebrating the victory of math and big data over pompous punditry.  Jeff Greenfield tweeted, “I, for one, welcome our new Algorithmic Overlord.”

At some level, I thrill to the ascendancy of math, and of math nerds – and I write this as a proud former math team captain (and math team T-shirt designer), and as someone whose very best summers as a teenager were spent in math (and writing) camp at Duke University.  It’s also one of the reasons I love Silicon Valley so much – it’s where nerds rule, and where even emerging VCs promote themselves as “Geeks.”

However, before we turn all of life over to algorithms, as some are suggesting, it’s important to place the election prediction in context.

The accomplishment of Silver’s splendid forecasting was to intelligently aggregate existing data, to accurately summarize the current, expressed intentions of the national electorate.  And we’ve learned that careful analysis is far more useful than blustery experts – something Philip Tetlock has been trying to tell us for years.

At the same time, all forecasting challenges are not created equal, and summarizing current public opinion is a much lower bar than predicting events far into the future – and Silver has been clear about this; it’s others who seem to be leaping ahead.

In many (arguably most) domains, and the further out you go, uncertainty likely swamps the ability of either experts or algorithims to make reasonable predictions – as Taleb, and more recently Mauboussin have discussed.

Discrediting expert predictions seems like real progress – but not if we believe the enduring lesson is to replace one group of fortune tellers with another.  We should certainly strive to use big data and careful math whenever we can – but let’s be careful not to fall into the trap of letting down our guard, and trusting all experts who come bearing algorithms.

David Shaywitz is co-founder of the Center for Assessment Technology and Continuous Health (CATCH) in Boston.  He is a strategist at a biopharmaceutical company in South San Francisco. You can follow him at his personal website. This post originally appeared in Forbes.