Of the three kinds of two-mean comparisons which judge a test statistic against a critical value taken from a Student t-distribution, one – the repeated measures or dependent-means application – is distinctive because it is meant to assess the value of a parameter which is not part of the natural order. This absence forces a choice between two interpretations of a significant test result and the meaning of the test hypothesis. The parallel universe view advances a conditional, backward-looking conclusion. The more practical proven future interpretation is a non-conditional proposition about what will happen if an intervention is (now) applied to each population element. Proven future conclusions are subject to the corrupting influence of time-displacement, which include the effects of learning, development, and history. These two interpretations are explored, and a proposal for new conceptual categories and nomenclature is given to distinguish them, applicable to other repeated measures procedures derived from the general linear model including ANOVA.



Recommended Citation

Gould, A. M., & Joullié, J.-E. (2017). 'Parallel Universe' or 'Proven Future'? The Language of Dependent Means t-Test Interpretations. Journal of Modern Applied Statistical Methods, 16(2), 200-214. doi: 10.22237/jmasm/1509495060