Statistics involves the development of methods and tests that are used to quantitatively define the variability inherent in data, the probability of certain outcomes, and the error and uncertainty associated with those outcomes. Some statistics are biased, some are based on opinions, and some are fabricated. A common misconception is that statistics provide a measure of proof that something is true. Instead, statistics provide a measure of the probability of observing a certain result. It is easy to misuse the statistics in data analysis even to the point of misconception because statistics do not introduce systematic error which can be introduced into the data intentionally or accidentally.
There are many associated variables in statistical numbers that the person analyzing the data does not see, and without further explanation or supportive data, one can easily come to the wrong conclusion and the scientist data could be presented as facts rather than probability. If the source from which the data was gathered was not factual, then this will reflect a statistic that is misleading, biased, and based on false information, but those persons who might later interpret the data had no idea that the source was not factual, and as a result, wrong information is publicized. Because statistics deal with numbers they often seem to be more convincing and less suspicious of false claims than descriptive arguments, but numbers can be easily manipulated in favor of someone’s opinion. In the last presidential election in the United States, there have been many misconstrued statistical data in the polls leading up to election day that gives a false reflection of the American public. From the statistical data, one would assume that only poor minority groups voted for President Obama and only white middle-class and rich people voted for Mitt Romney.
Prices start at $12
Prices start at $11
Prices start at $12
In many instances, people who were polled willingly set out to give false information of their intention, some people refused to reveal their intention, some were biased in their opinions, and not everyone was polled. If for example, 100 minorities were polled and 70 of the sample said they would vote for the president, then it is assumed that 70% of poor minority groups voted for President Obama, and if 100 people from the rich and middle class were polled and 60 of them said they would vote for Mitt, then it is assumed that 60% of the rich and middle class voted for Mitt Romney. The conclusion that only poor minority groups voted for Obama and only rich and the middle class voted for Mitt, would be wrong and misleading. When only a sample of the population is polled, the result does not reflect the general public and can therefore be misleading. Statistics that are misleading or misinterpreted can have a negative impact on individuals using the information because being armed with the wrong information can influence people to form biased opinions and make wrong choices which can be quite costly in their relationships, health, finance, education, or can create other setbacks.
Cite this page
This content was submitted by our community members and reviewed by Essayscollector Team. All content on this page is verified and owned by Essayscollector Team. All comments and user reviews are moderated by Essayscollector Team. In the case of any content-related problem, you can reach us through the report button.