In statistical research, experimental designs are used to investigate the effect of control variables on output responses. These methods are based on the assumption of normal distribution of data and face fundamental challenges in dealing with outliers. The present study examines five different examples of experimental design methods to deal with this challenge: Huber, quadratic, substitution, ranking, and fuzzy regression robustness methods. By providing empirical evidence from real data on seedling growth and weld quality, it is shown that fuzzy can be used as an efficient alternative to conventional methods in the presence of outliers. It is shown that fuzzy not only outperforms the classical experimental design method in the presence of outliers, but also outperforms standard robustness methods in handling outliers.