Confidence Intervals Make a Difference

Abstract

The use of confidence intervals (CIs) as an addition or as an alternative to null hypothesis significance testing (NHST) has been promoted as a means to make researchers more aware of the uncertainty that is inherent in statistical inference. Little is known, however, about whether presenting results via CIs affects how readers judge the probability that an effect is present in the population of interest and whether a replication would be likely to reveal the same results. In the present study, 66 PhD students were asked to interpret statistical outcomes presented as CIs or as conventional statistics ( t statistics and associated p values). Fewer misinterpretations of statistics—such as accepting the null hypothesis—and more references to effect size were found when results were presented as CIs. Furthermore, participants tended to be more certain about the existence of a population effect in the expected direction and about the replicability of the results when the results were presented following the conventions of NHST than when presented using CIs. Contrary to expectations, no evidence of a more precipitous drop in the belief of the existence of a population effect and replicability estimates when p values exceeded the significance level of .05 was found when data were presented using NHST instead of by CIs.