How Could Graphics and/or Statistics Be Used to Misrepresent Data
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 claim 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 give a false reflection of 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. In many instances, people who were polled willingly set out to give false information of their intention, some people refused to reveal
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 claim 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 give a false reflection of 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. In many instances, people who were polled willingly set out to give false information of their intention, some people refused to reveal