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  2. Sign test - Wikipedia

    en.wikipedia.org/wiki/Sign_test

    The sign test is a statistical method to test for consistent differences between pairs of observations, such as the weight of subjects before and after treatment. Given pairs of observations (such as weight pre- and post-treatment) for each subject, the sign test determines if one member of the pair (such as pre-treatment) tends to be greater than (or less than) the other member of the pair ...

  3. Question answering - Wikipedia

    en.wikipedia.org/wiki/Question_answering

    Question answering. Question answering ( QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP) that is concerned with building systems that automatically answer questions that are posed by humans in a natural language. [1]

  4. Tukey's range test - Wikipedia

    en.wikipedia.org/wiki/Tukey's_range_test

    Tukey's range test. Tukey's range test, also known as Tukey's test, Tukey method, Tukey's honest significance test, or Tukey's HSD ( honestly significant difference) test, [1] is a single-step multiple comparison procedure and statistical test. It can be used to correctly interpret the statistical significance of the difference between means ...

  5. Jarque–Bera test - Wikipedia

    en.wikipedia.org/wiki/Jarque–Bera_test

    Jarque–Bera test. In statistics, the Jarque–Bera test is a goodness-of-fit test of whether sample data have the skewness and kurtosis matching a normal distribution. The test is named after Carlos Jarque and Anil K. Bera . The test statistic is always nonnegative. If it is far from zero, it signals the data do not have a normal distribution.

  6. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    In particular, three data sets are commonly used in different stages of the creation of the model: training, validation, and test sets. The model is initially fit on a training data set, [3] which is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model. [4]

  7. Equivalence test - Wikipedia

    en.wikipedia.org/wiki/Equivalence_test

    Equivalence test. Equivalence tests are a variety of hypothesis tests used to draw statistical inferences from observed data. In these tests, the null hypothesis is defined as an effect large enough to be deemed interesting, specified by an equivalence bound. The alternative hypothesis is any effect that is less extreme than said equivalence bound.

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