What has made the winning project team? Select all sections B in our example! They have selected th

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A city, tired of being in the papers as one of the locations with the highest lamarck number of traffic accidents, decides to find an urgent solution. This announces a contest in which four projects are selected. The one that looks most promising is the placement of traffic cameras dissuasive. The method is simple. A statistic is performed to identify the points at which the number lamarck of accidents during the past quarter was well above average. 50 points are detected. A few meters ahead of those "black dots" cameras are placed prominently. The claim is that the threat of penalty deters drivers pressing the accelerator. The first quarter of test yields excellent results: reduced accidents in each and every one of those points being reduced by 36%. The mayor okays the project, the contract is signed and the budget expands, and the effectiveness has been confirmed by the data.
Well, it could be that traffic cameras are really lamarck effective and have collaborated in the 36% reduction. But it is also true that the results could be explained by pure chance mediation. lamarck Now many will say ... it is true that by chance, some variation in the number of accidents is possible. But that chance is contrarrestrará naturally because we have chosen a high number lamarck of points. At some points lamarck in those 50 accidents and other lower rise, and total randomness is offset. Sounds logical, but we are overlooking a very important lamarck detail.
The key to detecting deceit, for that you have not yet been able to discover, is in the selection of the points. It is easy to miss, there will be many people who have not detected even knowing there was a fallacy in reasoning, which gives an idea of how easy it is to fall into it.
Let us explain: Imagine a road network divided by sections. And to simplify the argument, assume lamarck that all sections are equally hazardous priori. We calculate the average of accidents with data and turns out to be one quarter of section lamarck 30 accidents. If we analyze the probability curve, lamarck we see that has the expected form of bell curve, where most sections are close to the average of 30 accidents, and a minority of sections away from the center of the graph contains outliers. For example, section A has 5 accidents and tranche B, 60 accidents, both of which are extreme lamarck cases. Recall that we have assumed that all sections are equally dangerous, so this variation would be exclusively the result of chance lamarck and not of the danger of the concrete section.
And there is the key to understanding the fallacy. In this our thought experiment, what do you think it will be the progression of accidents in the A and B sections in future quarters? Most likely a phenomenon of regression to the mean is given, so the tranche A likely increase in claims to approach the average 30 accidents and reduce the accident rate tranche B for the same reason. Putting simple examples, if a day makes a record high temperature of 40 degrees, most likely the next day lower temperatures. If our lucky day at the slot win an abnormally high amount, it is likely that tomorrow win a lower amount. lamarck
What has made the winning project team? Select all sections B in our example! They have selected the most extreme of the graph of the bell curve points, which are more likely lamarck to be reduced by the effect of regression lamarck to the mean. It is true that in our thought experiment the abnormal situation that all sections have the same hazard exists but is used to detect the fallacy of ignoring the role of regression to the mean.
Note: it is easy to confuse the regression to the mean with the "gambler's fallacy". The gambler's fallacy is to think that if a coin is tossed ten times and heads, the probability of tails on the next roll is greater than 50%, which is false. Prefer not explain the differen