The process of searching good parameter values is a non-trivial task for metaheuristic algorithms. When two algorithms are comparable in terms of speed and probability of convergence, the algorithm with less number of parameters is always preferred. This paper discussed the importance of the initial error covariance parameter, P(0), in Simulated Kalman Filter (SKF) with an intent to make SKF a parameter-less algorithm. To evaluate the importance of initial error covariance value in SKF, several values were selected and statistical analyses using nonparametric Friedman and Wilcoxon signed rank tests were carried out to see if different initial error covariance has any significant difference in the final outcome. The results prove that no matter what the initial error covariance is, SKF algorithm still managed to converge to near-optimal value without any significant degradation or improvement.