Fish stock assessment usually requires a wide range of supporting data, including an abundance index, production, age structure. However, some data are hardly available in many fisheries because of limited research funding and social attention. Therefore, many fisheries, particularly small-scale fisheries, often do not have sufficient data to support fish stock assessment and are considered data-limited or data-poor. An increasing amount of literature has been focused on the development of data-poor stock assessment methods in recent decades, among which electronic length frequency analysis (ELEFAN) is a prevalent method that uses length frequency distribution data to assess the status of fisheries. One crucial application of the ELEFAN is the estimation of growth parameters in the Von Bertalanffy growth function (VBGF). However, the method is based on certain optimization algorithms and cannot provide information on its precision or confidence intervals for growth parameters, which implies that the reliability of ELEFAN needs to be tested in future studies. This study used a bootstrap approach to evaluate the uncertainty of the ELEFAN method based on the survey data of in Haizhou Bay. This species is one of the dominant species in Haizhou Bay and plays an important role in the food web and ecosystem of the Yellow Sea. Although the declines in fishery resources have drawn increasing attention in many regions of the world, relevant studies have commonly focused on large-scale fisheries, whereas small-scale fisheries, such as that of , has been largely overlooked. Therefore, the biological characteristics of this species and their temporal changes is not well understood. This study was focused on the temporal changes in VBGF growth parameters of in Haizhou Bay at different survey periods. We conducted annual bottom trawl surveys in Haizhou Bay in the spring and autumn from 2013 to 2018, and used the ELEFAN method to estimate the VBGF growth parameters infinite length ( In addition, the bootstrapped ELEFAN was used to evaluate the variation in the growth parameters, and the difference was compared between 2013-2015 and 2016-2018. We analyzed the robustness of ELEFAN with respect to three aspects:(1) the effect of bin size of body length on parameter estimation, (2) the selection of different optimization algorithms (Simulated Annealing, SA; Genetic Algorithm, GA; Response Surface Analysis, RSA), and (3) the confidence intervals of parameter estimation through the bootstrap approach. The results showed that the VBGF growth parameters of in Haizhou Bay changed significantly during 2013-2018, and the decreased infinite length () indicated that there was a significant trend of miniaturization. The bin size of body length significantly affected the goodness of model fit and improper bin size settings might lead to unreasonable parameter estimations. Bootstrapped ELEFAN provided robust parameter estimations compared to the conventional ELEFAN approach, and the bootstrapped results were less affected by the randomness of sample data. The Genetic Algorithm could benefit from parallel computing in the TropfishR package, which significantly sped up computation. This study improved the understanding of population dynamics of . In particular, the changes of growth characteristics of this species may have a substantial impact on the Haizhou Bay ecosystem. We demonstrated that bootstrapped ELEFAN performed well and could be applied to the prevalent data-poor and small-scale fisheries.