Modelling carbon nanotube-polymer nanocomposites to predict their electrical conductivity demands high computational power. Past research usually assumed the conductive network follow a periodic pattern; however, the impacts of the underlying biases had never been investigated. This work provides insights to evaluate such biases and eliminate them to improve simulation accuracy.