Ninety-nine percent of the time, I believe in the use of statistics and data analysis to predict outcomes, make decisions or change minds. The goal of data analysis is to provide an educated guess at what could be an outcome or what is the best decision. Data analysis is very useful in most cases and should be utilized in just about every big decision or prediction. That being said, the problem with odds is there is always more than one outcome. The truth is, really anything can happen.

Nate Silver and his team are intelligent, reasonable people. From his perspective, there's really no reason his statistical methods won't lead him to another successful election prediction. Most of the time, his analyses have worked and it's reasonable to expect they'll work again. And that is where the logical flaw in his statistical analyses lies.

Right now, his Polls-Plus Forecasts have Clinton winning 79 percent in Iowa, Sanders winning 75 percent in New Hampshire, and Clinton winning 97 percent in South Carolina. If those odds of Clinton winning Iowa and New Hampshire turn out to be reality, Sanders is probably done. The real key for Sanders winning nationally is winning both Iowa and New Hampshire, as it's relatively agreed that would give him momentum going forward into further primaries and caucuses.

And this is where data analysis and odds all start to become fuzzy. Upsets happen. People just can't predict things like the 'Miracle on Ice' or catching a football with a helmet and then proceeding to win the Super Bowl. Upsets like that make statistics and predictive analysis seem irrelevant, but the truth is, there is always a chance that anything could happen.

Based on his forecasts, there's a 21 percent chance Sanders could win Iowa. But if Sanders actually does win Iowa and New Hampshire, the rest of Silver's state forecasts will change, which could result in his new model making Hillary Clinton the underdog. So Nate Silver could very well end up predicting Sanders as the Democratic nominee, but as of now, Silver is dead wrong.

This post originally appeared on Medium.