This paper discusses the need and importance of statistical power analysis in field-based empirical research in Production and Operations Management (POM) and related disciplines. The concept of statistical power analysis is explained in detail and its relevance in designing and conducting empirical experiments is discussed. Statistical power reflects the degree to which differences in sample data in a statistical test can be detected. A high power is required to reduce the probability of failing to detect an effect when it is present. This paper also examines the relationship between statistical power, significance level, sample size and effect size. A probability tree analysis further explains the importance of statistical power by showing the relationship between Type 11 errors and the probability of making wrong decisions in statistical analysis. A power analysis of 28 articles (524 statistical tests) in the Journal of Operations Management and in Decision Sciences shows that 60% of empirical studies do not have high power levels. This means that several of these tests will have a low degree of repeatability. This and other similar issues involving statistical power will become increasingly important as empirical studies in POM study relatively smaller effects.