With limited resources, scientific inquiries must be prioritized for further
study, funding, and translation based on their practical significance: whether
the effect size is large enough to be meaningful in the real world. Doing so
must evaluate a result's effect strength, defined as a conservative assessment
of practical significance. We propose the least difference in means
(δL​) as a two-sample statistic that can quantify effect strength and
perform a hypothesis test to determine if a result has a meaningful effect
size. To facilitate consensus, δL​ allows scientists to compare effect
strength between related results and choose different thresholds for hypothesis
testing without recalculation. Both δL​ and the relative δL​
outperform other candidate statistics in identifying results with higher effect
strength. We use real data to demonstrate how the relative δL​ compares
effect strength across broadly related experiments. The relative δL​ can
prioritize research based on the strength of their results.Comment: 5 figures. arXiv admin note: substantial text overlap with
arXiv:2201.0123