The Salp Swarm Algorithm is a popular optimization method known for its simplicity and efficiency. However, it is susceptible to structural bias, which can cause the algorithm to favor specific regions of the search space without regard for the objective function. Structural bias can hamper exploration, leading to the population revisiting certain locations without acquiring new information, which adds to the computational load. This study involves a comprehensive investigation of the occurrence and types of structural bias in the Salp Swarm Algorithm. Additionally, we evaluate two newly developed variants of Salp Swarm Algorithm, namely the Laplacian Salp Swarm Algorithm and the Quadratic Approximation Salp Swarm Algorithm, for their structural bias. To detect and analyze structural bias and its type, a simple yet effective methodology called the signature test is employed. After conducting a thorough analysis, we have identified algorithms that have demonstrated unbiased behavior. We anticipate that our analysis will be a valuable resource for practitioners who are interested in analyzing the theoretical aspects of their algorithms.