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By using information from the analysis of data from a first data source for modeling data from a second source, or how to analyze them in parallel. One should also not underestimate technical problems like model misspecification, limited number of replicates, limited computing time or the use of asymptotical test statistics. One has to outweigh the influence of the different factors when planning as well as when interpreting the results, elements of research which are often missing. 26 3 Edler, L.

5 First Author’s epilogue When I started my career in biostatistics at the German Cancer Research Center three decades ago it took only a few months to realize the importance and relevance of computational statistics for both the research work in applied statistics and the biostatistical consulting of clients and partners coming from all relevant biomedical fields of experimental and clinical cancer research. At that time - just after the appearance of John Tukeys book on Explorative Data Analysis (Tukey (1977) resources on computational statistics methods and literature were rare.

1977): Exploratory Data Analysis. Addison-Wesley, New York. The Laws of Coincidence David J. uk Abstract. Anomalous events often lie at the roots of discoveries in science and of actions in other domains. Familiar examples are the discovery of pulsars, the identification of the initial signs of an epidemic, and the detection of faults and fraud. In general, they are events which are seen as so unexpected or improbable that one is led to suspect there must be some underlying cause. However, to determine whether such events are genuinely improbable, one needs to evaluate their probability under normal conditions.

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