Lie With Statistics

How to Lie with Statistics Summary

There are some people that rely heavily on the statistical information provided by the media, government, and other research groups in order to form opinions or come to a conclusion on a particular idea or product. However they fail to realize that a lot of the time the data is manipulated in such a way that leads them to believe something that is not actually the case. Statistics can lie in many ways the first way is by using a sample that has a bias. For instance, the data collected would only be of one particular group of people, but they would claim it was the population. Another way data is manipulated is through averages. The data will be presented as the average, but the type of average that is
The sample is supposed to represent the general population, however this is rarely the case because of the biases that lie with in sampling. For instance, the people that you interview could tend to lean towards one specific group of people. In the Yale example on page sixteen, the people that did not make a lot of money could be harder to find and interview than the rich people that have been successful. The richer people are going to be more likely to be found and answer the questionnaire, which will therefore skew the data. In addition, people could also lie about their income; some may overstate it and others could understate it. Furthermore, this was also the case in the example of the Literary Digest, their poll with regards to the election was not accurate, because the only people that they could reach to poll were the rich, because they had telephones and magazine subscriptions, and that particular group of people was biased towards the Republican Party. In many other cases, biases can be created when the person that is being interviewed is not telling the truth. We have no way of telling if the reports are from honest people. Moreover, people that are polling others could also manipulate data, because they are more likely to lean towards a certain group of people when choosing whom to give the questionnaire. There are several biases that could leave the reader to believe something that is not true. The
Using the same data points and calculating a different average and not specify which average was used can completely lead someone to believe something that is not true. When something is stated as an average you do not exactly know as much as you think you know about the data, unless it is specified which kind of average is used. For instance, the mean, median, and mode are the different types of averages that can be used each representing something completely different. Taking the mean of salaries can completely misrepresent the average salary, because there is only a small amount in the high end that can be really big and bring the average up very high. For instance, in the neighborhood example on page 32, the average that was taken in order to sell a home was the mean and the buyer was lead to believe that the average salary was way higher than it actually was, because of the few millionaires that make it appear as if the community as a whole has a higher