Ratios and effect size.

Abstract

Responding to a related pair of measurements is often expressed as a single discrimination ratio. Authors have used various discrimination ratios; yet, little information exists to guide their choice. A second use of ratios is to correct for the influence of a nuisance variable on the measurement of interest. I examine 4 discrimination ratios using simulated data sets. Three ratios, of the form a/(a + b), b/(a + b), and (a − b)/(a + b), introduced distortions to their raw data. The fourth ratio, (b − a)/b largely avoided such distortions and was the most sensitive at detecting statistical differences. Effect size statistics were also often improved with a correction ratio. Gustatory sensory preconditioning experiments involved measurement of rats’ sucrose and saline consumption; these flavors served as either a target flavor or a control flavor and were counterbalanced across rats. However, sensory preconditioning was often masked by a bias for sucrose over saline. Sucrose and saline consumption scores were multiplied by the ratio of the overall consumption to the consumption of that flavor alone, which corrected the bias. The general utility of discrimination and correction ratios for data treatment is discussed.