To ensure reliable predictions for various applications, it is essential to understand the impact of Gaussian and non-Gaussian noise on data-driven modeling. Gaussian noise has been a convenient assumption for model development, but real-world scenarios sometimes defy Gaussian assumptions. Our research compared the performances of the direct and parametric data-driven modeling methods with Gaussian and non-Gaussian sensor noise. The direct data-driven modeling methods that are evaluated are direct data-driven methods with pulse input-output dataset and direct data-driven methods with step input-output dataset. The parametric models that are evaluated are the Output Error (OE), Autoregressive with Moving Average and Exogenous Input (ARMAX), Autoregressive with Exogenous Input (ARX) and Box-Jenkins model (BJ). The result shows that the performance of the direct and the parametric data-driven modeling methods deteriorates under non-Gaussian noise.