Much research in educational psychology concerns group differences. In this study, we argue that Bayesian estimation is more appropriate for testing group differences than is the traditional null hypothesis significance testing (NHST). We demonstrate the use of Bayesian estimation on gender differences in students' achievement goals. Research findings on gender differences in achievement goals have been mixed. We explain how Bayesian estimation of mean differences is more intuitive, informative, and coherent in comparison with NHST, how it overcomes structural and interpretive problems of NHST, and how it offers a way to achieve cumulative progress toward increasing precision in estimating gender differences in achievement goals. We provide an empirical demonstration by comparing a Bayesian and a traditional NHST analysis of gender differences in achievement goals among 442 7th-grade students (223 girls and 219 boys). Whereas findings from the two analyses indicate comparable results of higher endorsement of mastery goals among girls and higher endorsement of performance-approach and avoidance goals among boys, it is the Bayesian analysis rather than the NHST that is more intuitively interpreted. We conclude by discussing the perceived disadvantages of Bayesian estimation, and some ways in which a consideration of Bayesian probability can aid interpretations of traditional analytical methods.