Repeating experiments is an important instrument in the scientific toolbox to validate previous work and build upon existing work. We present two concrete use cases involving key techniques in the NLP domain for which we show that reproducing results is still difficult. We show that the deviation that can be found in reproduction efforts leads to questions about how our results should be interpreted. Moreover, investigating these deviations provides new insights and a deeper understanding of the examined techniques. We identify five aspects that can influence the outcomes of experiments that are typically not addressed in research papers. Our use cases show that these aspects may change the answer to research questions leading us to conclude that more care should be taken in interpreting our results and more research involving systematic testing of methods is required in our field.