Biomedical researchers often encounter the large-p-small-n situations—a great number of variables are measured/recorded for only a few subjects. The authors propose a fuzzy permutation method to address the multiple testing problem for small sample size studies. The method introduces fuzziness into standard permutation analysis to produce randomized p-values, which are then converted into q-values for false discovery rate controls. Monte-Carlo simulations show that the proposed method has desirable statistical properties when the study variables are normally or non-normally distributed. A real dataset is analyzed to illustrate its use. The proposed fuzzy permutation method is recommended for use in the large-p-small-n settings.