Abstract
The polarity coincidence correlator (PCC), a nonparametric hypothesis testing procedure, is presented as an alternative to the Pearson product-moment correlation coefficient (r). Previous theoretical results indicate that the PCC statistic should be inferior to the correlation coefficient when the data are normal, but it may outperform r for non-normal data with a highly peaked probability density function. Application of the PCC to a human factors experiment is illustrated. For this particular application, the PCC performance compares favorably to the product-moment correlation test when the data are normal and exceeds that of the correlation test for non-normal data. The results, combined with the ease of PCC computation, support the PCC as a promising test for human factors experimentation.
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