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 taken is not given. For example is it the arithmetical average, median, or mode that is being used to present the data. This can completely skew the data one way or another. Furthermore, when data is presented the presenter can lie by leaving out certain things that will usually go unnoticed by the reader. In addition, many people make a big deal about something that doesn’t matter when using statistics, which leads the reader to believe that whatever the made a big deal about actually is significant. There could be a difference that is so tiny that it doesn’t have importance, however leaving out the range of error could also be a way of lying to the reader. The final two ways to lie with statistics are through pictures and graphs. Graphs can be easily manipulated and are easy to make someone think something that is not true. In addition, pictures can change scale and the comparison of two things could appear to be different than it actually is. Lying with statistics has lead the general public to believe several things that aren’t actually true even though the research claims that it is proven. Statistics can use a bias sample, pick a misleading average, make a big deal about something that is irrelevant, leave out key information, and manipulate graphs and pictures in order to make the audience believe something that is false.
Sampling can completely distort data and mislead the reader. 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 presenter may state that the average of the general population is x, however it may only be represent able of a certain group in the general population.
Biases within sampling are not the only way people lie with statistics, they also can choose a certain type of average. Using the same data points and calculating a different average and not specify which average was used can completely...
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