Statistical equivalence methods have been in development since the late 1980s in order to provide an appropriate statistical methodology to address nondifferences in biological experiments. This is analogous to genetic association studies in which a polymorphism "is not associated" with a trait. We applied the equivalence method to genetic data to confirm that an association between the KIF1B (kinesin family member1B) rs10492972 allele and multiple sclerosis (MS), reported in Nature Genetics in 2008, is present neither in eight data sets of cases and controls nor in three independent data sets of the International Multiple Sclerosis Genetic Consortium. When the data sets are considered together, a nonsuperiority test excludes the rs10492972*C allele as a major "risk" allele for MS with a high degree of confidence (P = 1.18 × 10(-4) ). We propose that equivalence methods are more appropriate for stating that a polymorphism does not contribute to disease susceptibility. If an equivalence test applied to genetic data sets fails to reveal an association based on standard methods, it demonstrates that there is no genetic association-i.e., the absence of evidence is evidence of absence. When reporting genetic association based on a cohort of a limited size, caution is needed regardless of how attractive the underlying biological rationale is. The data gathered for KIF1B in MS also underscore the need for very large sample sizes with the appropriate equivalence statistical methods in order to exclude reported false-positive results.