comparative experiments

comparative experiments

[kəm′par·əd·iv ik′sper·ə·məns]
(statistics)
Experiments conducted to determine statistically whether one procedure is better than another.
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The S/N ratios of all the column comparative experiments suggest that smaller particle size is better than the larger particle size.
In Section 3, comparative experiments are conducted to show the performance of the proposed features.
We have also done comparative experiments with m set to the other suggested values, and the results are consistent.
The ROC curves from the comparative experiments on these two kinds of data sets using ensemble learning (compared with SVM) are as those in Figures 4 and 5.
The first chapter introduces terminology and the most common statistical models used in comparative experiments.
Bailey wrote in Design of Comparative Experiments, "For a given illness, if there is already a standard drug that is known to be effective, then it is not ethical to give no treatment in a clinical trial of a new drug for that illness.
Results from comparative experiments demonstrated that the CLIA-validated test had high accuracy, precision, sensitivity and specificity in identifying Notch1 activating mutations in FFPE tumour tissues and blood clinical samples.
The tutorial progresses from simple comparative experiments and experiments with a single factor, through robust parameter design, experiments with random factors, and nested and split-plot designs.
Because it is always possible that a confounding prior cause has not been identified and observed, this strategy has a fundamental limitation not shared by randomized comparative experiments.
Comparative experiments were also done on Subject A and C with chloroform and diethyl ether by asking them to inhale the vapours of chloroform and diethyl respectively for 5 sec and compare the brain effect felt with those in the two sets of experiments described above.
Comparative experiments dating back to the 1930s have compared different forms of psychotherapy, contrasting theoretical orientations (for example, behavioral versus psychodynamic), methods (for example, insight-oriented versus supportive), and structures (for example, individual versus group or family).
Statistical methods in this book include: variance components analysis, variance transmission analysis, risk-based control charts, capability and performance indices, quality planning, regression analysis, comparative experiments, descriptive statistics, sample size determination, confidence intervals, tolerance intervals, and measurement systems analysis.

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