Significance tests have their place

Null-hypothesis significance tests (NHST), properly used,tell us whether we have sufficient evidence to be confident of the sign of the population effect-but only if we abandon two-valued logic in favor of Kaiser's (1960) three-alternative hypothesis tests. Confidence intervals provide a useful addition to NHSTs, and can be used to provide the same sign-determination function as NHST. However, when so used, confidence intervals are subject to exactly the same Type I, II, and III error rates as NHST. In addition, NHSTs provide two pieces of information about our data-maximum probability of a Type III error and probability of a successful exact replication-that confidence intervals do not. The proposed alternative to NHST is just as susceptible to misinterpretation as is NHST. The problem of bias due to censoring of data collection or publication can be handled by providing archives for all methodologically sound data sets, but reserving interpretations and conclusions for statistically significant results.