A formal psychometric analysis shows that apparent psychological profiles of large language models are primarily driven by response bias, not actual traits. This bias, which causes models to consistently favor one end of a scale, accounts for 81-90% of between-model variation, far exceeding human differences. The study concludes that these profiles are artifacts of instrument design and not true model properties, urging the development of assessments based on response orthogonality.