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This paper gives emphasis on the importance of testing the performance of signal processing algorithms with real data and learning from the data. The discussion is made primarily within the context of a patient monitoring research. The author suggests six steps on how to test with and learn from real data. These steps are: (1) minimize measurement uncertainty so your physiological phenomenon is consistently observable, (2) understand your data, (3) acquire a significant number of appropriate training and testing sets, (4) construct high-performance validation criteria, (5) design algorithms that can generalize in the clinical environment, and (6) test and learn from your data. Results show that this process is also applicable to other fields as well.