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. The $25,111 average salary is true only for the time period when the statistical tests were undertaken, but it may not be true when the same statistical tests were taken 30 years prior to the current Yale statistical tests. A similar test conducted 20 years after the current statistical tests will generally show a different statistical finding. 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.

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. To ensure that statistical outcomes are reliable, the manager must determine that there is no distortion or manipulation of the raw statistical data gathered. Someone may have intentionally changed or manipulated