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The high volume of data resulting from a rapidly increasing number of experiments in materials science necessitates an efficient preparing of the data before any analysis. In addition, due to the large datasets in some experiments, it is essential to reduce the data sample to a small number of representative data points. In this study, three statistical methods for the change-point analysis are tested for the automated selection of representative creep data which provides large possibilities to speed up the data preparation for their further analysis. Moreover, this approach aids the practitioner to produce consistent and unique representative data for each experiment more efficiently.