Bicistronic reporter assay systems have become a mainstay of molecular biology. While the assays themselves encompass a broad range of diverse and unrelated experimental protocols, the numerical data garnered from these experiments often have similar statistical properties. In general, a primary dataset measures the paired expression of two internally controlled reporter genes. The expression ratio of these two genes is then normalized to an external control reporter. The end result is a 'ratio of ratios' that is inherently sensitive to propagation of the error contributed by each of the respective numerical components. The statistical analysis of this data therefore requires careful handling in order to control for the propagation of error and its potentially misleading effects. A careful survey of the literature found no consistent method for the statistical analysis of data generated from these important and informative assay systems. In this report, we present a detailed statistical framework for the systematic analysis of data obtained from bicistronic reporter assay systems. Specifically, a dual luciferase reporter assay was employed to measure the efficiency of four programmed -1 frameshift signals. These frameshift signals originate from the L-A virus, the SARS-associated Coronavirus and computationally identified frameshift signals from two Saccharomyces cerevisiae genes. Furthermore, these statistical methods were applied to prove that the effects of anisomycin on programmed -1 frameshifting are statistically significant. A set of Microsoft Excel spreadsheets, which can be used as templates for data generated by dual reporter assay systems, and an online tutorial are available at our website (http://dinmanlab.umd.edu/statistics). These spreadsheets could be easily adapted to any bicistronic reporter assay system.