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On the other hand, Pascal implicitly assumed that a negative experimental result would have disconfirmed the hypothesis that his experiment was designed to test because, by analogy with the claim that 'absurd' consequences disconfirm mathematical hypotheses, he failed to realize that scientific experiments test clusters of interconnected hypotheses rather than individual hypotheses.
SaTScan™ employs Kulldorff's spatial scan statistic (16– 18) to identify and test clusters of childhood mortality.
It employs Kulldorff's spatial scan statistic to identify and test clusters of mortality.
To evaluate the output of this software tool, sets of test clusters were created and graphically rendered.
To measure the uniformity of generated clusters, 10 test clusters were created with 100 points in each cluster.
Primary (i.e., most likely) and secondary clusters were detected by using the log-likelihood ratio test; clusters with maximum log-likelihood ratios were considered primary.
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We compared test cluster variance by using CV to limit the variable scale effects.
Benchmarking and model derivation can be done using a small test cluster based on new hardware.
The requirement to test cluster stability also for varying data sets leads to several approaches to extend the idea of comparing partitions resulting from non-equal, but overlapping data sets [33], [36], or even disjoint data sets [34], [37].
For example, our test cluster is made up of 8 nodes with 32 CPU cores per node.
Based on the Mann–Whitney U test, Cluster I and Cluster II were significantly different in terms of their tumor grades (W = 206; P = 0.0059).
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