When participants respond in the same way to stimuli of two categories, responses are often observed to be faster when both stimuli are presented together (redundant signals) relative to the response time obtained when they are presented separately. This effect is known as the redundant signals effect. Several models have been proposed to explain this effect, including race models and coactivation models of information processing. In race models, the two stimulus components are processed in separate channels and the faster channel determines the processing time. This mechanism leads, on average, to faster responses to redundant signals. In contrast, coactivation models assume integrated processing of the combined stimuli. To distinguish between these two accounts, Miller (1982) derived the well-known race model inequality, which has become a routine test for behavioral data in experiments with redundant signals. In this tutorial, we review the basic properties of redundant signals experiments and current statistical procedures used to test the race model inequality during the period between 2011 and 2014. We highlight and discuss several issues concerning study design and the test of the race model inequality, such as inappropriate control of Type I error, insufficient statistical power, wrong treatment of omitted responses or anticipations and the interpretation of violations of the race model inequality. We make detailed recommendations on the design of redundant signals experiments and on the statistical analysis of redundancy gains. We describe a number of coactivation models that may be considered when the race model has been shown to fail.