The U.S. government keeps statistics on many people in America. One interesting statistic is the poverty rate

 



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
Alternative Answer
Unfortunately, this is a very broad subject because statistics that are quoted and presented to us on a regular basis are often misleading.  Anything from political statistics to consumer information can be skewed simply by misuse of statistical data or by poor methods used to gather the data to begin with.  According to a Forbes article on the subject, “Beyond the obvious candidates like suggestions of correlation implying causation and improper use of statistical techniques, perhaps one of the greatest enablers of fake news …is sampling bias and selective definitions” (Leetaru, 2017) .

One subject that is notorious for being prone to data misuse is crime statistics.  We are often fed alarming information saying crime is rising or assuaged saying crime is declining, but crime statistics are much more complex than that.  Is it ALL crime that is on the rise (unlikely), or only certain crimes?  White collar crimes, violent crimes, etc?  A rise in homicides is a much different issue than a rise in identify theft, for example.

It is our job as consumers of information to be critical thinkers and check the sources of studies when we are alarmed by information that we are presented with before being reactive. “As the United States grapples with an epidemic of gun violence and these reverberating impacts of homicide, it will take better research, consistent data collection and community-tailored approaches to understand and address the roots of violent crime. In the meantime, we can all benefit from a more critical, humane and nuanced understanding of how complicated crime data really is” (Krishnakumar, 2021).

Source:

Leetaru, K. (2017, February 3). Lies, damned lies and statistics: How bad statistics are feeding fake news. Forbes. https://www.forbes.com/sites/kalevleetaru/2017/02/02/lies-damned-lies-and-statistics-how-bad-statistics-are-feeding-fake-news/?sh=27b90c9850ca