Data of dynamic processes plays an increasingly important role in process monitoring and optimization of injection molding processes. On the basis of two experiments, it is shown that the sampling rates have a large influence on derived information, which can be generated from this data. It is shown that high sampling rates are important for the calculation of integral values that are frequently used for process monitoring. A qualitative estimation of the uncertainties is performed at different sampling rates for the injection pressure integral. Advanced process monitoring systems based on machine learning algorithms use process data as input to predict the molded part quality. In the second experiment, a model is presented which uses only the injection flow and injection pressure profile as input and achieves high coefficients of determination for the prediction of the part weight, despite the absence of mold sensor data and scalar data. It is shown that higher sampling rates of dynamic data results in higher prediction quality of these models. The presented results enable users to estimate a lower bound for the sampling rates of dynamic data for their use cases. For the first time, a prediction model for part quality is presented, which is only based on the time series data of injection pressure and injection flow. This improves the understanding of the data needed for high quality machine learning models of injection molding processes.