Using two or three different modeling techniques on the same data and then comparing predicted outcomes across the different models is called ________.

• A. Ensemble
• B. Cross-Validation
• C. Triangulation
• D. Bootstrapping

Answer: (C)

Using two or three different modeling techniques on the same dataset and then comparing their predictions is triangulation. The idea is to corroborate findings by approaching the data from multiple, distinct methods and looking for converging results. If different models—each with its own assumptions and biases—point to the same pattern, you gain confidence that the result reflects something real rather than a quirk of a single method. This is different from ensemble methods, which combine outputs to make one final prediction, and from cross-validation or bootstrapping, which focus on estimating generalization performance or variability rather than cross-checking conclusions across methods. Triangulation emphasizes validation through agreement across diverse modeling approaches.