To help minimize errors and confusion in the scientific literature, a few principles can be followed. The original intent and hypothesis of each study should be made clear, and deviations from the initial purpose should be stated. The hypothesis-testing portions of the research need to be clearly differentiated from the hypothesis generating sections. If data were selected and reanalyzed in unintended ways, the analyses should be clearly identified as hypothesis generating, and no conclusions should be drawn from such data. The data should be unassailable. The control group should be adequate. The data analysis should be consistent with the experimental design. Given that the larger the number of P-values, the greater the rate of false declarations, the total number of derived P-values should be reasonable and should be reported. The publication should unveil all details necessary to understand and replicate the research project, including the data analysis. Alternate etiologies should be seriously considered in light of potential confounding factors. It is important to be critical of the results of a study, especially when data confirm a preconceived hypothesis.