**Lichtman's Model Wrong: Election Outcome**

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Post on Nov 07, 2024

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Lichtman's Model Wrong: Why the Election Didn't Go as Predicted

Remember all those articles and news segments about how Alan Lichtman's model was predicting a Trump victory? Yeah, that didn't happen. Lichtman's model, this thing that supposedly predicts presidential elections with almost uncanny accuracy, totally choked on this one.

So, what gives? Was the model just plain wrong? Or is there something we missed?

Well, it's a bit of both.

How Lichtman's Model Works

Lichtman's model is based on thirteen key factors, like the state of the economy and the popularity of the incumbent party. Each factor is either positive or negative for the incumbent party.

Six or more negative factors means the incumbent party loses, while five or fewer means they win.

Why Did It Go Wrong This Time?

This time, Lichtman's model predicted six negative factors, suggesting a Trump victory. But Biden won, handily at that.

Why the mismatch?

• The model is built on historical patterns, which don't always perfectly predict the future.
• This election was unlike any we've seen before. A global pandemic, social unrest, and a deeply divided nation all played a huge role.

Some folks argue that Lichtman's model is overly simplistic, and that it doesn't take into account enough variables to be truly reliable.

Looking Ahead

The failure of Lichtman's model is a reminder that predictions are never guaranteed, especially in a rapidly changing world. We shouldn't rely solely on models, but instead look at a wider range of factors when trying to understand elections.

So, what can we learn from this? Don't get too hung up on the predictive power of any single model. The future is complex, and human judgment still plays a crucial role.