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Understanding Statistics: Misuse and Misrepresentation (Article Sample)


Companies an organizations often miuse and misrepresent statistics to suit their narrative. Particularly in the us, companies are notorious for this data manipulation. Use two case studies from the past 7 years to discuss to cases of misuse and misrepresentation of statistics reported in the media and give reasons as to why and how they could have corrected this.


Understanding Statistics: Misuse and Misrepresentation
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Understanding Statistics: Misuse and Misrepresentation
Businesses and researchers rely on statistical analyses to make informed decisions supported by evidence instead of assumptions. Advanced technology enhances this stalwart of sound judgments in today’s business environment in the form of analytics and intelligence software. As such, businesses, organizations, and researchers can easily solve troubling predicaments using this insight. Nevertheless, statistics can also exist as fake or misleading information and data that provide unreliable insight and information. When businesses use such data, they misunderstand their target clientele since the unique customer identifier values are false. The misrepresentation of information occurs due to people, companies, and organizations misunderstanding, misreading, and wrong comparison or definition of data and definitions. This paper examines New Yorks Times assertions of a booming air travel industry at the height of the COVID-19 pandemic and former president Trump’s 2015 bogus statistics on crime reports in the US as epitomes of statistical misunderstanding and misuse.
Primarily, companies and individuals manipulate and misuse statistics to tell, support or disavow a or their narrative on an issue. Such manipulation is intended to rally the masses or lead them into disbelieving already proven facts. As such, this form of manipulating data selectively reports information or forthright gives false data that at first glance is believable (). Notably, people, companies, and organizations in the US manipulate statistics from renowned agencies and state corporations to give an alternative storyline. Former President Trump was notorious for these antics. For example, Bump (2015) reported that President Trump had tweeted a bogus infographic on crime in America. It alluded to information obtained from the Crime Statistics Bureau in San Francisco and claimed that black-on-black killings were more than police-on-black killings at 97% and 1% respectively. It also claimed that 81% of all white killings were perpetrated by blacks and police killings of whites stood at a mere 3%. Astonishingly, Trump tweeted this information after a Black Lives Matter Movement supporter was roughed up at a rally. This statistical misrepresentation polarized people further and widened the racial divide across the US; yet the president should be a symbol of unity, oneness, and racial integration. To correct this statistical misuse, the president should have relied on data from credible government agencies, such as the FBI and the NSA. The president would have avoided shaming the government by quoting make-believe figures from unreliable agencies. As such, companies, organizations, the media, and even government representatives and agencies use manipulated data and information to support their assertions, just like Trump did in 2015 when reporting crime rates in America.
Media organizations have a responsibility to present factual information in a non-partisan manner supported by factual data and figures. They should avoid overgeneralizing, partisan reporting, and alarming headlines or articles that outright fail to give the categorical state of affairs. The New York Times is no stranger to such reporting just as Hessney (2020) explains. She debunks a previous New York Times article that drew attention to a 123% rise in air travel when the pandemic officially began. Although Irwin (2020) had come to the right statistical conclusion about the percentage change in air travel, he erred in his deductions, claiming the industry was booming. Despite using the right percentages, he failed to account for the mitigating factors for this growth according to Hessney (2020): a bearish market and a gripping global pandemic that forced people to travel back home. It was a case of inappropriately using percentages to show change. Therefore, the author should instead have expressed this change in a better parameter of measurement, worded it better, and accounted for this change in air travel to a global viral pandemic.
Overall, statistics is now a more critical aspect of the business decision-making and information analysis process than it was ever before. These corporations and businesses rely on statistical analys

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