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Typically, it is fairly easy to add iterations to performance tests to increase the total number of measurements collected; the best way to ensure statistical significance is simply to collect additional data if there is any doubt about whether or not the collected data represents reality. Whenever possible, ensure that you obtain a sample size of at least 100 measurements from at least two independent tests. Although there is no strict rule about how to decide which results are statistically similar without complex equations that call for huge volumes of data that commercially driven software projects rarely have the time or resources to collect, the following is a reasonable approach to apply if there is doubt about the significance or reliability of data after evaluating two test executions where the data was expected to be similar. Compare results from at least five test executions and apply the rules of thumb below to determine whether or not test results are similar enough to be considered reliable: 1. If more than 20 percent (or one out of five) of the test-execution results appear not to be similar to the others, something is generally wrong with the test environment, the application, or the test itself. 2. If a 90th percentile value for any test execution is greater than the maximum or less than the minimum value for any of the other test executions, that data set is probably not statistically similar. 3. If measurements from a test are noticeably higher or lower, when charted side-byside, than the results of the other test executions, it is probably not statistically similar. ssrs ean 128 SSRS GS1-128 / EAN-128 Generator - OnBarcode
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