The U.S. government keeps statistics on many people in America. One interesting statistic is the poverty rate. To be living in poverty, one must earn income below a certain threshold (approximately $900 per month). Many multimillionaires are included in this statistic. Recently, Barbara Streisand was “living in poverty.” In a particular year, she did not perform live, and her album sales were extremely slow. She has a great deal of wealth but had little income that year. Although she has more money than 99.99% of the rest of the population, according to the government income threshold, she was considered to be impoverished. What other statistic can you name that is misleading? Why?

Verified Expert Answer

Statistics play a vital role in determining the current state of affairs within a country or a region. Through statistics, we can easily make predictions that impact society even in the long run (Makridakis et al., 2018). However, when the information is not accurate, statistics can be a source of confusion. As indicated, one of the statistics that at times can provide misleading interpretation of the statistics on income levels, where for those who are not on payroll, the statistics capture them to live below poverty rates which in a real sense may not be the case.

Another example is the unemployment rate. with unemployment rate, the statistic usually does not factor in everyone who does not have a job, and therefore not an accurate measure of joblessness. As such, statistics experts often use real unemployment rates to estimate the real statistics on joblessness (Cardoso & Ferreira, 2009). The common unemployment rate often fails to consider various groups of people when making calculations. For example, it does not consider whether an individual is working full-time or part-time.

It also fails to consider persons who are working as freelancers and those in informal employment. Therefore, based on these aspects, the unemployment rate as a statistical figure can be misleading.

References

Cardoso, A. R., & Ferreira, P. (2009). The dynamics of job creation and destruction for University graduates: why a rising unemployment rate can be misleading. Applied Economics, 41(19), 2513-2521.

Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PloS One, 13(3), e0194889. https://doi.org/10.1371/journal.pone.0194889