How can graphics and/or statistics be used to misrepresent data?
Data Graphics and statistics can be used to misrepresented in a variety of ways. To an extreme extent as Americans we consume alot because advertisements and peer pressure convince us to. Through persuasive numerical facts, the public tends to believe these claims to be scientifically sound. Data can be misrepresented through human error such as sampling errors which are the most common (Groves, 2004). Sampling errors occur due to a diference between the sample and population.
Where have you seen this done?
For example, the U.S. Senate’s Permanent Subcommittee on Investigations disclosed an internal Goldman Sachs email containing the results of one such due diligence report (naic).
According to naic, the email lists a number of serious errors uncovered by due diligence. For example:
“– approx 7% of the pool has material occupancy mis-representation where borrowers took out anywhere from 4 to 14 loans at a time and defaulted on all.
– approx 62% of the pool has not made any payments (4% were reversed pymts/nsf [non-sufficient funds]).
– approx 5% of the pool was possibly originated fraud-ulently based on the dd [due diligence] results. Main findings: possible ID theft, broker misrepresentations, straw buyer, and falsification of information in origina-tion docs.
The above disparities indicate the credit characteristics of the loans were materially different from those described by the initial loan tape.
Groves, R. (2004). Survey Errors and Survey Costs. New York: Wiley-Interscience. Retrieved from h³ps://books.google.com/books? hl=en&lr=&id=BnmGMtMAo_oC&oi=fnd&pg=PR13&dq=Groves,+Robert.+(2004). +Survey+Errors+and+Survey+Costs.+New+York:+Wiley-Interscience.&ots=EOZ2- ´xLiM&sig=´d6cVVrJ±3OBJfgvqkwISr8WnC0#v=onepage&q&f=false
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