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A/B testing is basically a comparison of two outputs, generally when only one variable has changed: run a test, change one thing, run the test again, compare the results. This is more useful with more small-scale situations, but very useful in fine-tuning any program. With more complex projects, multivariant testing can be done.
Alpha testing is simulated or actual operational testing by potential users/customers or an independent test team at the developers' site. Alpha testing is often employed for off-the-shelf software as a form of internal acceptance testing before the software goes to beta testing. [56]
The technique uses hypothesis testing to accept a model if the difference between a model's variable of interest and a system's variable of interest is within a specified range of accuracy. [7] A requirement is that both the system data and model data be approximately Normally Independent and Identically Distributed (NIID).
The alpha phase usually ends with a feature freeze, indicating that no more features will be added to the software. At this time, the software is said to be feature-complete. A beta test is carried out following acceptance testing at the supplier's site (the alpha test) and immediately before the general release of the software as a product. [5]
A/B testing (also known as bucket testing, split-run testing, or split testing) is a user experience research method. [1] A/B tests consist of a randomized experiment that usually involves two variants (A and B), [ 2 ] [ 3 ] [ 4 ] although the concept can be also extended to multiple variants of the same variable.
Finally, the test data set is a data set used to provide an unbiased evaluation of a final model fit on the training data set. [5] If the data in the test data set has never been used in training (for example in cross-validation), the test data set is also called a holdout data set. The term "validation set" is sometimes used instead of "test ...
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Although the 30 samples were all simulated under the null, one of the resulting p-values is small enough to produce a false rejection at the typical level 0.05 in the absence of correction. Multiple comparisons arise when a statistical analysis involves multiple simultaneous statistical tests, each of which has a potential to produce a "discovery".