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An irreverent look at the t test.

Even rather common statistical tests tend to confuse and disconcert young investigators, students, and statistically naive readers. During the course of 4 decades, I have helped people figure out why analytic results turned out statistically significant or not. Investigational premises, both for the researcher and his audience, change as thinking evolves over the span of a project. Furthermore, examined data sets typically present unexpected and possibly intriguing values for deeper consideration. This editorial is intended to remind readers that there are overarching concerns even in the application of a t test. t tests are among the most commonly used and recognized of applied inferential statistical tests. When we want to know if the average response to 1 treatment differs by more than chance from the average response to a comparison treatment, we apply a t test. Understanding how t tests work and, more importantly, how we work with them, has several benefits. If we clarify how we approach and work with 2 means, we better appreciate how to deal with more complex models. The issues we confront when we evaluate t tests will pertain also to a panoply of other statistical applications. In fact, issues governing investigator opportunities and responsibilities should be understood as complemented by similar concerns considered by the consumer of research findings, namely, the reader. Statistical textbooks and formal instruction help with choosing a reasonable test and using it appropriately. Some cautious thought, however, may encourage a better appreciation of the full value of your data, whether the data look good or bad. It is too easy to compromise responsibility by assigning trust to the mechanics of pseudomathematical procedures that may not be fully understood. Statisticians and researchers (both active investigators and their readers) frequently suffer from distant, strained, and sometimes uncomfortable relationships with one another. The statistician’s approach to data may reflect perspectives and values apparently in conflict with the priorities of the investigator. Researchers typically want expedient answers so they can move ahead with their work, whereas statisticians seem to try to assure obeisance to arcane rules. Even when the investigator and the statistician are one and the same person, these possibly competing points of view invite trouble, maybe even more often than they induce compromise. The prevalence of computers and user-friendly statistical packages has markedly increased in recent years. Correspondingly, most statistical analyses are completed locally, in-house. Computation has typically become a routine exercise, a possibly mechanical chore assigned to someone lacking a comprehensive understanding of the investigational context. Often, there are few opportunities for recognizing that there is more than 1 applicable method, and that some procedures will clarify certain aspects of the work while obfuscating others. It used to be that a researcher would bring in a statistical consultant to help with both computational and statistical issues. Whatever people learned in their training, combined with the professional statistician’s help, would lead to a paper ultimately subjected to cogent journal review. Something like widely accepted standards characterized the thinking behind the evaluation of statistical tests. Today, however, data analysis follows closely stereotypical possibly computergenerated advice. This expedience fits well with the rush to apply some conventional statistic, often a t test, as soon as a trial concludes. What is lost is shared responsibility among investigators, reviewers, and readers, watching out for problems and missed chances inherent in a given statistical comparison. In contemporary settings, it is unfortunately not possible to count on everyone doing his part. For too many readers and investigators, a simple check that statistics were done and reported has come to appear sufficient. Sometimes a healthy appreciation of one’s own data may be subverted by the pressure of time. Peer review also has become increasingly compromised with individual researchers substituting perfunctory computation for sound judgment. One’s peers accede to expedience by mimicking one another’s approaches. Imitated methods tend to prevail within subdisciplines, whereas GUEST EDITORIAL