Research Paper on:
Real World Application of Inferential Tests

Unformatted Sample Text from the Research Paper:
example of this would be when a person leaves school and attempts to understand the professional manuals or to do some research for their employer. Consequently, the specific words we  use to communicate results of data analyses can have an impact on others (and our own) understanding of the meaning of statistical tests. Evidence indicates that  many scientists fail to accurately interpret the meaning of common statistical analyses used to test hypotheses. A solution to this problem may very well be in the way inferential statistical  tests are applied in the real world. A quick example might be to see if people who exercise in the morning are actually more efficient at their jobs during  the day. If one were to merely examine the productivity of the employee in relationship to the workout in the morning, then this would be considered a correlational study and  not an inferential one. However, if one were to measure those who workout in the morning as opposed to those who did not, and then compared their productivity in the  workplace, this would then be an inferential test. Suppose it was found that eighty-six percent of the people who worked out were more productive, with a mean mark of  seventy-nine percent for those who did not work out. This would reduce the inferences to only two possible choices: Exercising in the morning before work increases productivity, or the difference  in productivity is purely coincidental. Now, given that these are the only two choices, one could consider using a null hypothesis, which would, point blank, state that exercise before work  has no effect on productivity. The experiential hypothesis would state the other end of the spectrum, which is that exercise definitely increases the productivity of the employee. Unfortunately, neither of