Human populations around the world encounter various environmental challenges and, consequently, develop genetic adaptations to differentselection forces. Identifying the differences in natural selection between populations is critical for understanding the roles of specific genetic variants in evolutionary adaptation. Although numerous methods have been developed to detect genetic loci under recent directional selection, aprobabilistic solution for testing and quantifying selection differences between populations is lacking. Here we report the development of aprobabilistic method for testing and estimating selection differences between populations. By use of a probabilistic model of genetic drift andselection, we showed that logarithm odds ratios of allele frequencies provide estimates of the differences in selection coefficients betweenpopulations. The estimates approximate a normal distribution, and variance can be estimated using genome-wide variants. This allows us to quantify differences in selection coefficients and to determine the confidence intervals of the estimate. Our work also revealed the link between genetic association testing and hypothesis testing of selection differences. It therefore supplies a solution for hypothesis testing of selectiondifferences. This method was applied to a genome-wide data analysis of Han and Tibetan populations. The results confirmed that both the EPAS1 and EGLN1 genes are under statistically different selection in Han and Tibetan populations. We further estimated differences in the selectioncoefficients for genetic variants involved in melanin formation and determined their confidence intervals between continental population groups. Application of the method to empirical data demonstrated the outstanding capability of this novel approach for testing and quantifying differences in natural selection.