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However, interaction effects\\nare typically very small and often fail to replicate. In this study, we conducted a simulation\\ncomparing the generalizability and estimability of two linear regression models: one correctly\\nspecified to account for interaction effects and one misspecified including simple effects only. We\\nmanipulated noise levels, predictor variable correlations, and different sets of regression weights,\\nresulting in 9216 different conditions. From each dataset we drew 1000 samples of N = 25, 50,\\n100, 250, 500 and 1000 resulting in a total of 55.296.000 analyses for each model. Our results\\nshow that misspecification can drastically bias regression estimates, sometimes leading to zero or\\nreversed simple effects. Furthermore, we found that when models are generalized to the entire\\npopulation, the difference between the explained variance in the sample and in the population is\\noften smaller for the misspecified model than for the correctly specified model. However, the\\ncomparison between models shows that the correctly specified model explains the data at\\npopulation level better overall. 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