Chapter 1: Our Treacherous Tendency

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Discreet Deceit

There is a huge tendency of human beings to insert bias, and the strangest thing about this tendency is that it occurs sometimes without us even realizing it. One major contributor of this bias is our pride. We all desire to look good for other people, to look like we've got it together, and even to twist the truth in order to preserve our reputation and successful appearance. The results of some surveys and statistics simply cannot be trusted due to the nature of the content in which they seek to gather information about. Here is an example. After a campaign had been launched to reduce government spending in Florida by cutting the workforce by 5%, numbers came in that said Florida had the smallest workforce in the entire country (117 employees per 10,000 residents, whereas national average is 215 per 10,000 residents). However, a question regarding employment to gather information about whether cuts should be made is bound to bring in results that may not be completely truthful. Why? Because nobody wants to admit to having a job if there's a chance it will be taken from them for admitting to it! There is great insecurity and distrust of the government, so it's hard to tell what to do with results like these. Surveyors can hardly tell who to believe. Our pride and confidence in our jobs is on the line, and results of these kinds of surveys are hardly useful. These types of bias can be significantly reduced simply by making the surveys more anonymous, being extremely cautious of wording, or using open-ended questions to allow people to answer however they would like, instead of giving them a cafeteria-line of options they can choose from.

Shady Statistics

It is also important to note that it is sometimes the samples of people who participate in surveys and statistics that are at fault for what many people call the 'built-in bias'. If I wanted to gather information about whether customers enjoyed shopping at a particular store in the mall, I would not gather my sample from the people that are already inside that store. Chances are, if they are shopping there, they like it. One example involves a statistic that states, "Only 12% of Americans know Obama cut taxes last year." However, the sample was taken only from survey volunteers that were associated with the Tea Party, and the results were used to generate a statistic that was deceivingly unrepresentative of the entire country. The outcomes were biased before anybody even answered the question! Surveyors must be extremely cautious when it comes to how a survey is set up and how the results are gathered. Conducting a survey about teenagers' opinions whose information is gathered via text messaging eliminates those teens who a) don't have a cell phone and b) don't have text messaging. Furthermore, the survey only obtains results from those who choose to participate! This is sometimes only a small fraction of those who were asked, and results like these can do dangerous things to any statistics that are calculated.

Drawing the Wrong Conclusions

We also need to be aware of what many call the 'overly-exact statistic'. "Studies show that people wash their hands 4.67 times a day." In a scenario like this, we should ask ourselves, "How in the world did they get that figure?" Will a person, no matter how randomly selected they are, ever admit to occasionally not washing their hands to a complete stranger? These kinds of statistics are only useful in determining what people say about washing their hands. We can hardly draw any other conclusions. These kinds of tendencies--and treacherous tendencies at that--to make conclusions about biased data can originate from a problem in the sample themselves. A random sample was once described as a sample "selected by pure chance from the 'universe,' a word by which the statistician means the whole of which the sample is a part." Stratified sampling is the best kind of sampling. It allows the sample to consist of the same proportions of things as they exist in reality. If 7% of a school's attendees were of Native American descent, then 7% of those who are surveyed should also be of Native American descent. However, biased tendencies even exist in this kind of sampling. Basically, polls no longer need to be rigged...sometimes we sway the results on our own. And most of the time, we have no idea.

 

 

For more insights on how to eliminate bias, here is another example.