The process of hypothesis testing encompasses several areas that could potentially lead to unethical activity, beginning with the null and alternative hypotheses. In the hypothesis-testing approach, the preliminary assumption is that the null hypothesis is true. If a re-searcher has a new theory or idea that he or she is attempting to prove, it is somewhat unethical to express that theory or idea as the null hypothesis. In doing so, the researcher is assuming that what he or she is trying to prove is true and the burden of proof is on the data to reject this idea or theory. The researcher must take great care not to assume that what he or she is attempting to prove is true.Hypothesis testing through random sampling opens up many possible unethical situations that can occur in sampling, such as identifying a frame that is favorable to the outcome the researcher is seeking or using nonrandom sampling techniques to test hypoth-eses. In addition, the researcher should be careful to use the proper test statistic for tests of a population mean, particularly when is unknown. If t tests are used, or in testing a population variance, the researcher should be careful to apply the techniques only when it can be shown with some confidence that the population is normally distributed. The chi-square test of a population variance has been shown to be extremely sensitive to the assumption that the popula-tion is normally distributed. Unethical usage of this technique oc-curs when the statistician does not carefully check the population distribution shape for compliance with this assumption. Failure to do so can easily result in the reporting of spurious conclusions.It can be unethical from a business decision-making point of view to knowingly use the notion of statistical significance to claim business significance when the results are not substantive. Therefore, it is unethical to intentionally attempt to mislead the business user by inappropriately using the word significance.

The art and science of sampling has potential for breeding unethical behavior. Considerable research is reported under the guise of random sampling when, in fact, nonrandom sampling is used. Remember, if nonrandom sampling is used, probability statements about sampling error are not appropriate. Some re-searchers purport to be using stratified random sampling when they are actually using quota sampling. Others claim to be using systematic random sampling when they are actually using con-venience or judgment sampling.In the process of inferential statistics, researchers use sample results to make conclusions about a population. These conclusions are disseminated to the interested public. The public often as-sumes that sample results truly reflect the state of the population. If the sample does not reflect the population because questionable sampling practices were used, it could be argued that unethical re-search behavior occurred. Valid, representative sampling is not an easy task. Researchers and statisticians should exercise extreme caution in taking samples to be sure the results obtained reflect the conditions in the population as nearly as possible.The central limit theorem is based on large samples unless the population is normally distributed. In analyzing small-sample data, it is an unethical practice to assume a sample mean is from a normal distribution unless the population can be shown with some confidence to be normally distributed. Using the normal distribu-tion to analyze sample proportions is also unethical if sample sizes are smaller than those recommended by the experts.

Using sample statistics to estimate population parameters poses a couple of ethical concerns. Many survey reports and advertisers use point estimates as the values of the population parameter. Often, no error value is stated, as would have been the case if a confidence inter-val had been computed. These point estimates are subject to change if another sample is taken. It is probably unethical to state as a conclu-sion that a point estimate is the population parameter without some sort of disclaimer or explanation about what a point estimate is.The misapplication of t formulas when data are not normally distributed in the population is also of concern. Although some studies have shown that the t formula analyses are robust, a re-searcher should be careful not to violate the assumptions underly-ing the use of the t formulas. An even greater potential for misuse lies in using the chi-square for the estimation of a population vari-ance because this technique is highly sensitive to violations of the assumption that the data are normally distributed.

Referring to the Various ethical issues discussed above..... Which do you feel to be more important, especially in dealing with human subjects, and why?