Accuracy is a Critical Aspect of Statics (Research Paper Sample)
DATA IS A USEFUL TOOL BUT IS SOMETIMES MISUSED. WHAT ARE SOME OF THE WAYS THROUGH WHICH DATA CAN BE MISUSED OR MISUNDERSTOOD.
Accuracy is a critical aspect of statics.Factors such as degrees of freedom and significance level are applied to leave room for margin of error in derived figures. Nevertheless, statistical information is often misinterpreted because of deliberate actions, wrong choice of definitions, or misunderstanding of the data
Accuracy is a Critical Aspect of Statics
Accuracy is a critical aspect of statics. Factors such as degrees of freedom and significance level are applied to leave room for margin of error in derived figures. Nevertheless, statistical information is often misinterpreted because of deliberate actions, wrong choice of definitions, or misunderstanding of the data. Limited information regarding Canada’s early 2020 Covid-19 statistics and Oregon’s error in using vital diversity statistics highlight the challenges of misinterpreting data.
Excess death rates were a subject of statistical information misinterpretation in Canada. In 2020, StatCan computed excess deaths, which measured the severity of Covid-19 by alleging that the pandemic contributed to the exceeding deaths compared to the average of past years. Surprisingly, the calculation of excess deaths based on data from January to the end of March suggested a negative death trend as 1145 fewer deaths than expected were reported (Flanagan, 2020). However, for regions such as New York City, excess deaths data was summed up to May 2020. Therefore, the different timeframes contributed to the discrepancy since other areas, such as New York, recorded positive excess deaths. Covid-19 was not highly prevalent until mid-May when deaths and new infections were skyrocketing. Therefore, excess death was misinterpreted in Canada due to not accounting for the timeframe when the pandemic was hitting hard, similar to other territories.
On another note, Oregon’s use of incomparable definitions when accounting for its population’s racial composition represents the actual diversity in the state. US census data notes that the white alone population is 86.7% (Mapes, 2020). Numerous news outlets identified Oregon’s white populace with this figure without accounting for the section of Latinos who identify as white. Many Latinos in the USA term themselves as white; hence it is possible to disregard the population diversity of the state if the section of non-Hispanic whites remains unexamined. Notably, mixed-race individuals account for this confusion and balloon the white population figure, thus understating other races. The same case also manifests in the Black and Asian communities. Illustratively, a mixed-race individual with a Black origin is automatically categorized as Black, whereas Latinos are not. Therefore, the failure to account for the section of non-Hispanic whites creates a misinterpretation of the diversity in Oregon’s population.
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