<p>A zero-failure test minimizes both the number of required samples and the test duration needed to ensure component reliability. Currently, most companies use the classical zero-failure test method, based on frequentist statistics, to determine the necessary sample size during reliability demonstration test planning. This method assumes that the shape and scale parameters are unknown and treated as constants—a strict assumption that can be difficult to meet when life testing is limited. In contrast, the Bayesian approach treats these parameters as unknown random variables, assigning probability distributions to reflect uncertainty of parameter estimation, offering a more realistic assumption. In this study, we numerically estimated the required sample size for zero-failure tests using the Bayesian method across various shape and scale parameters and compared the results with the classical method. While the Bayesian method generally requires a larger sample size, under specific conditions, we observed an unexpected phenomenon where it required fewer samples than the classical method. We identified the limitation of the Bayesian method for zero-failure tests and proposed criteria for its application. This study also provides guidelines for selecting between the classical method and the Bayesian method when planning zero-failure tests.</p>