How to lie in Statistics Essay Example | Topics and Well Written Essays - 1000 words

How to lie in Statistics - Essay Example I feel statistical information presents the facts as they are. The characteristics of the samples are made to show the characteristics of the entire population under study. The sample’s statistical results are generally assumed not to represent the characteristics of those who are not part of the population. For example, the $25,111 salary represents the average salary of people chosen for the statistical tests (such as people in Yale alone). However the $24,111 salary does not represent the people not chosen for the survey, such as the people working in Alaska (14). Likewise, the $25,111 average salary is true only for the time period when the statistical tests were undertaken. However, the $25,111 average salary may not be true when the same statistical tests were taken 30 years prior to the current Yale statistical tests. Likewise, a similar test conducted 20 years after the current statistical tests will generally show a different statistical finding (18). Interpreting the difference in the findings, the statistical findings should not be taken as occurring in ALL situations; to do so would be a lie. It is a lie because interpreting the statistical results is all-encompassing would be too twisted, exaggerating, oversimplified, or distorted. Sales people would use the average results of statistical test to convince the prospective buyers to purchase their wares; the sales persons are willing to lie to generate sales. Consequently, many buyers are persuaded by the statistical test results to buy the sales person’s products and services. The buyer wants to join the â€Å"band wagon† by buying what the average person wants to buy (103). I feel the author (9) correctly emphasized the statistical data can show the validity of the first sentence â€Å"There’s a mighty lot of crime around here†. However, such interpretations are subject to correction. For example, statistical data showing the number of crimes committed in one neig hborhood can be more persuasive to the leaders when compared to absence of statistical information on the same topic. In fact, the average person can easily draw up several theories based on common sense or statistical trends. However, the trends are high probabilities (not100 percent assurance) of future outcomes. Further, I correctly understand that statistical information correctly presents quantitative as well as qualitative figures as basis for decision making. The manager can base one’s expansion policy on the statistical figure stating there is a huge profit. However, the manager must beware of false statistical figures. The statistical computation of the gross profit figure may be based on erroneous data. Likewise, the manager must ensure that the mathematical computation of the statistical results is accurate (112). To ensure that statistical outcomes are reliable, the manager must determine if the there is no distortion or manipulation of the raw statistical data ga thered. Someone may have intentionally changed or manipulated the real statistical data to suit selfish or biased needs. The spoiled statistical data will generate unrealistic statistical findings. The unrealistic statistical findings will trigger unprofitable management decisions (133). I think statistics generally shows facts that are based on real outcomes. The records of the Connecticut Tumor Registry show that cancer survival has increased due to the