Bad Statistic
September 17, 2019
Bad Statistic
A bad statistic refers to the misuse of either useful or un-useful numerical data. The information provided to the reader misleads the reader in case he has no much information about the statistics. The reliability of statistics is crucial in ensuring the precision of a particular analysis. There are various techniques to ensure the reliability of statistic is high such as the use of control tests that are required to produce similar results when an experiment using the same conditions is performed spss assignment topics for college students.
Misleading statistics may be as a result of faulty polling, which refers to how the questions are phased and therefore affecting how the target population responds to the questions. For instance, a poll trying to establish people’s opinions about the taxation by the government, the person conducting the survey may frame questions in a manner like, “Should the government tax the employed persons so that others do not work” or “How do you view the taxation by the government”. The two questions drive to one point but may be misunderstood. Therefore it’s very crucial to frame the questionnaire appropriately.
The media is mostly involved in delivering misleading statistics. For instance, on twenty-ninth of the year two thousand and fifteen, the Republicans from the US Congress questioned Cecil Richards on the misusage of $500 million in annual federal funding. The representative Jason Chaffetz explained using pink to represent breast exam reduction and the color red to represent the increase in the number of abortions. The chart structure shows that there was an increase in the number of abortions while the number of cancer screening had gone down. A closer examination of the chart, however, reveals that the chart lacks a defined y-axis and therefore this means that there is no definite justification for the placement of measurement lines.
The chart is an example of a lousy statistic as it lacked a clear y-axis. It is not possible to get a clear picture of the information that was intended in the chart. The error could have been intentional to conceal some information, or the person who drew the chart could have omitted the y-axis by mistake.
Works cited
https://www.datapine.com/blog/misleading-statistics-and-data/
