To test the theories that drive technical communication research, investigators may statistically analyze data gathered for descriptive or experimental studies. In such analyses, investigators often set a very small statistical risk of rejecting a true null hypothesis of no relationship between variables to avoid subsequently incorrectly accepting an alternative hypothesis that there is a relationship. By this normal procedure, investigators may unwittingly lower the statistical power to reject a false null hypothesis of no relationship, and, thereafter, they may incorrectly fail to accept the statistically alternative hypothesis that there is a relationship. Our purpose is to demonstrate how to use a statistical table for planning ahead to gain acceptable power and how to report the power fully in the results. Even after an experiment has been completed, investigators can still estimate and report the power. Careful attention to power contributes to more meaningful tests of theories, and good reporting gives readers a clearer picture of the meaning of the tests.

Professional communicators reporting the results of experiments often need to express the fact that the differences in the data were not statistically significant and that the null hypothesis could not be rejected. It is suggested that communicators may find it difficult to express these results partly because failing to reject a null hypothesis is not the same as accepting a null hypothesis. Writers may report failing to reject a null hypothesis in any of five ways: directly and briefly; directly with the exact level of statistical probability stated; directly with the confidence interval specified; directly with an explanation of not claiming causation; and directly with a discussion of possible reasons.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>