Abstract

Attempts by 20th century philosophers of science to define inductive concepts and methods concerning the support provided to scientific theories by empirical data have been unsuccessful. Although 20th century philosophers of science largely ignored statistical methods for testing theories, when they did address them they argued against rather than for their use. In contrast, this study demonstrates that traditional statistical methods used for validating computer simulation models provide tests of the scientific theories that those models may embody. This study shows that these statistical methods are applicable regardless of whether the scientific theory or model is deterministic, stochastic, or chaos-based in nature. Traditional statistical methods, such as hypothesis testing, test theories by providing measures of their goodness-of-fit with the portion of the world the theories describe. Nothing more is needed to portray the agreement between theory and the world. Furthermore, the statistical measures of goodness-of-fit are not ampliative inductive measures. Therefore, the ultimate value and direction of the 20 th century ampliative inductive program within philosophy of science is thrown into question. This study also suggests that advanced statistical, visualization, and hybrid quantitative qualitative methods hold promise for offering scientists additional methods for testing scientific theories through validating their computer model representations. As a prerequisite to the above discussions, this study proves that scientific theories, when applied to concrete physical systems, can indeed be represented by computer models