Options for more powerful human-factors/ergonomics independent-groups studies: Increasing dependent variable reliability

Increasing dependent variable reliability ρ(y,y) will directly reduce sample size requirements and the associated costs of independent-groups studies. This somewhat surprising result is analytically considered in terms of the classical Z-test comparison of two means representing independent samples of size (N) from populations with common variances. Analysis reveals that the dependent variable reliability directly trades-off with sample-size in its impacts on sensitivity, so that constant statistical power is maintained by reducing sample-size while proportionally increasing ρ(y,y). Three illustrations (typically reflecting sample-size and associated cost reductions of ~50% or more) are presented to demonstrate the value, as measured in cost benefits, of the systematic efforts to enhance reliability in a human factors/ergonomics framework. These examples include: (a) "Integrating performance measures" in the evaluation of worker exposure-effects, (b) "Quality and preferences" in new product development, and (c)"Individual differences assessments" in the BEES Test Battery. Test-Retest Reliability analyses are recommended as means to evaluate (1) Potential for enhancing statistical power via increases in ρ(y,y) and (2) Impacts of attempts to increase statistical-power via increases in dependent-variable reliability (ρ(y,y)).