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
mostly liberal viewpoints as oppose to the average populations viewpoints (STATABUS, 2016). Another example is putting a survey in a gun magazine that would more likely result in positive response to gun support. Misleading questions is another type of bad statistics.
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,
2016). An outlier is an individual value that falls outside the overall pattern of the data (Dressler, 2010). Michael Jordan has a geography degree from the University of North Carolina and makes seven hundred thousand a year from the National Basketball Association (NBA). The University of North Carolina states that the starting average salary graduation from UNC was two hundred and fifty thousand dollars while the average starting salary for someone with a geography degree is twenty thousand (Dressler, 2010). This is misleading since Michael Jordan did not obtain this income from using his degree but was incorporated into the data. Michael Jordan is considered an outlier. His earnings do not accurately convey what most geographers would earn. Michael Jordan’s basketball career should not have raise the average salary of geography graduates (Paret, 2012). The average is accurate but the statistical numbers should have included the median along with the information. One solution to this problem is to ensure people who do not earn their salary from their degree should not be incorporated into the data pertaining to that field. In conclusion, statistics are valuable information to the world. They can be very persuasive to consumers. However, not all statistics tell the full truth. Some statistics can be misleading with poorly collected data. As a result, it can be inconclusive. Statistics can be manipulative with fallacy. We as consumers must be critical thinkers of any statistical numbers, even if it appears to be true.