This symposium will introduce and discuss how scholars can improve upon statistical significance tests, which continue to constrain the production of knowledge in management science. The extensive use of these tests in quantitative research has led to the accumulation of ""statistically significant"" results that are both too small to be practically relevant and so small that they are unlikely to replicate. A field that aspires to provide useful advice to managers needs to focus on practically important and robust effects. The proposed symposium introduces and discusses alternative approaches to overcome the limitations of statistical significance tests - such as, effect size measures, confidence intervals, graphs, meta-analyses, baseline modeling and the implications of these approaches for the accumulation of scientific knowledge. A final ""Question and Answer"" session will offer additional opportunities for further discussions, advice and recommendations.