Examples Of Misleading Statistics

Statistics is everywhere. We consume this information from political campaigns to medical studies. It is constantly on our television and even on the radio on our way to work. It lets us know what is going on in the world. However, statistics can be misleading and be used improperly. Some can even be bias to promote an agenda or belief. As critical thinkers, bad statistics can be debunked and avoided. Emily Dressler’s ‘Don’t Be Fooled By Bad Statistics’ informs the viewers that we should avoid misleading statistics (Dressler, 2010). While statistics help consumers to stay informed with the current world, it can also be used improperly due to poorly collected data, leading questions, and fallacy. Poorly collected data can lead to misleading results (Dressler, 2010). Results can be inconclusive if the data collected does not represent the population of interest. An example of a bad data collection can be putting a survey form in a magazine that is mostly read by liberals. As a result, it will obtain
It should be carefully worded and not elicit any bias (Dressler, 2010). Bias can lead to misleading results based on sample selection and data collection. Majority of people will often time believe statistics if it comes from a researcher or someone with great credentials to speak about a certain subject. Who are we to disagree with a professor or scientist that has vast knowledge in that field? An example of a misleading question is ‘Do you feel you should pay taxes so lazy people can sit at home all day and do nothing?” More than likely, the person responding will say no. However, if you rephrase the question to “Should our government help people who are having trouble finding work?’ More than likely, it would get a positive answer (TruthPizza, 2015). Questions should not encourage an answer of what we would want from the respondent. How the question is worded can have a major impact on the result (Ramsey,