Understanding Statistical Information (Research Paper Sample)

Understanding Statistical Information
Statistics are indispensable to everyday human activities as they provide evidence-based information to help make informed decisions. Thus, it is critical not only to have accurate data but to interpret it correctly to be able to make the right decisions. However, users often misinterpret data due to deceptive visual representation or flawed correlation. Fox News’s misleading visual representation of religious trends in the U.S. and the spurious correlation between gasoline prices and overweight cases in Australia exemplify some of the challenges of misrepresenting data.
The rate at which Americans were becoming non-religious was an example of statistical data misrepresentation by Fox News. In 2021, the program “Tucker Carlson Tonight” used a chart that visually implied that half of the Americans who were religious in 2009 and become non-religious by 2019. However, the change between 2009 and 2019 was just 12%, but the chart created the impression that the decline was by 50% (Carlson, 2021). The reason for this misperception is the decision to truncate the Y-axis, making the 12% decline to appear more significant than it actually is. Such actions are deliberate and intended to persuade the audience to believe that Christianity was losing its relevance to Americans’ lives. Notably, the source of the data was a conservative religious group, whose goal could have been to challenge viewers to embrace religion.
. Another case of misrepresenting information was a study in Australia that used flawed correlations. In the study, the researchers observed that the number of overweight cases had reduced in the country following an increase in the prices of gasoline. This observation led to the conclusion that the increase in gas prices had forced commuters to walk or cycle to their workplaces, resulting in weight loss (Prakash et al., 2022). While this argument is viable, it is not foolproof as there could have been other valid reasons for the reduction in obesity cases. For instance, more people could have started to eat healthy or go to the gym. In this regard, the researchers failed to consider the effect of confounding factors, which could also have the same effect as the increase in gas prices (Kahlert et al., 2017). Such omissions are the cause of flawed correlation whereby researchers overlook the influence of external factors in a cause-effect relationship between two variables. As such, users of the information could make wrong decisions, for example, by supporting a government policy to increase gas taxes with the goal of encouraging more people to walk or cycle to walk as a remedy for overweight cases.