  At every phase of scientific research, scientists must decide how to allocate
limited resources to pursue the research inquiries with the greatest potential.
This prioritization dictates which controlled interventions are studied,
awarded funding, published, reproduced with repeated experiments, investigated
in related contexts, and translated for societal use. There are many factors
that influence this decision-making, but interventions with larger effect size
are often favored because they exert the greatest influence on the system
studied. To inform these decisions, scientists must compare effect size across
studies with dissimilar experiment designs to identify the interventions with
the largest effect. These studies are often only loosely related in nature,
using experiments with a combination of different populations, conditions,
timepoints, measurement techniques, and experiment models that measure the same
phenomenon with a continuous variable. We name this assessment contra-analysis
and propose to use credible intervals of the relative difference in means to
compare effect size across studies in a meritocracy between competing
interventions. We propose a data visualization, the contra plot, that allows
scientists to score and rank effect size between studies that measure the same
phenomenon, aid in determining an appropriate threshold for meaningful effect,
and perform hypothesis tests to determine which interventions have meaningful
effect size. We illustrate the use of contra plots with real biomedical
research data. Contra-analysis promotes a practical interpretation of effect
size and facilitates the prioritization of scientific research.
