The Misuse of Biased-Samples to Confuse

Samples can be of great use in approximating some set of characteristics of the overall population or in determining the outcome of events (e.g. elections) from opinions, but these samples can easily be misleading due to bias. Even when all aspects of the test are controlled for, biases can still be built-in to the sample. Don’t allow yourself to be fooled by biased samples, even if the study itself seems genuine.

Samples may be biased in that they are not representative of the entire population, so be mindful of that when reviewing studies that employ samples. There’s usually some aspect of the population always left out and one can only extrapolate so much from their particular sample. Suppose a liberal magazine polls their readers on their political opinions and their sample reports that most people in their sample are in favor of a liberal political leader. The magazine may predict that a liberal political leader would be elected, but the fallacy here is that they’re sampling a subsection of the population that does not represent the total views of all voters. Take another example: a social scientist is interviewing random people at a grocery store around 6pm about their job satisfaction and concludes, from these opinions, that most people are happy with their jobs. Do you see the issue? Interviewing at 6pm excludes people who may be working late shifts, something which may certainly have a bearing on their job satisfaction. The particular grocery store may be expensive or inexpensive, so that will exclude people of different incomes that work elsewhere. Lastly, the part of the city, the city itself, and the region they decide to conduct these interviews in excludes other places with individuals who may very well have a different set of issues that they face. One needs to look no further than the difference between a small town that revolves around a mining industry and a bustling city the revolves around tourism to understand why this can skew the data. It is in this light that biases can be part of the sample, so you should always be mindful of who the sample excludes and how this may not reflect the overall attitudes of the population.

Samples will always exclude some part of the population and it’s just a matter of whom. Ask yourself who the sample is excluding and ask yourself what views are not equally being represented. Then, ask yourself if the sample can really accurately reflect what the persons conducting the study say they can. If the data is extrapolated too far, the study is bound to be inaccurate because it will exclude relevant perspectives and persons.


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