What I Was Wrong About This Year Article Analysis

The article, ‘What I Was Wrong About This Year” by David Leonhardt, explains the misconceptions and realities of probabilities, mainly through a number of real life examples. Leonhardt begins with an initial example, the probability of a “full-scale war” occuring in Syria. Initially, Israel used classic intelligence reports, but, after years of leaders manipulating these reports to justify any event occurring, they decided to present leaders with estimated probabilities of different events. With this new approach, a report concluded that a single scenario would increase the event of a war by 10%, declaring the Syrian war to have a conditional probability. This was likely calculated by a more complex version of P(AlB), yielding the probability
They assumed, as so many do, in so many instances in our lives, that, because the probability of this event happening, and provoking a war to commence, was so miniscule, there was no chance that it could occur at all. This war clearly incorrect. Leonhardt explains that people have a difficult time visualizing probabilities with simply numbers, they need a story. He argues that, despite his previous belief, the best response is not explanation and context; these probabilities must be communicated more effectively, “It is not enough to say an event has a 10% probability. People need a story that forces them to visualize the unlikely event - so they don’t round 10 to 0.” Leonhardt uses an example about a polling for candidates to illustrate his claim. A “forecast”, or a poll taken of sample of the original population, predicts Candidate X a 10% chance of winning. Though this shows statistically significant support for the alternative candidate to come out victorious, this prediction would also supply the readers of an explanation as to how errors could have occurred in the sample observation, including a winning map for X, allowing people to visualize the complement to occur, with an X victory. While it may not come without its own faults of potentially provoking confusion, this new method could be one step in essential
This was increasingly so as I considered my own actions and reactions to similar situations described. I too often round the probability of a single event occur to either 0% or 100%, not entirely considering that it is not definite either way. For instance, take the example Leonhardt described about the most recent presidential elections. Americans were told that Hillary clinton had a 72 - 85% chance of winning the presidency. This, clearly, did not mean that there was no way she would win. However, hearing this statistic, based on statistics calculated from polls taken from a number of samples, I went to bed, the night of the election, assuming that, without a doubt, Clinton would win. As we can assume, I was extremely surprised the next morning. I had rounded that 72-85% chance to a 100% chance of a Clinton victory. However, this is not the only instance that this concept has related to events in my