enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Pre- and post-test probability - Wikipedia

    en.wikipedia.org/wiki/Pre-_and_post-test_probability

    Pre-test probability and post-test probability (alternatively spelled pretest and posttest probability) are the probabilities of the presence of a condition (such as a disease) before and after a diagnostic test, respectively. Post-test probability, in turn, can be positive or negative, depending on whether the test falls out as a positive test ...

  3. Likelihood ratios in diagnostic testing - Wikipedia

    en.wikipedia.org/wiki/Likelihood_ratios_in...

    In evidence-based medicine, likelihood ratios are used for assessing the value of performing a diagnostic test. They use the sensitivity and specificity of the test to determine whether a test result usefully changes the probability that a condition (such as a disease state) exists. The first description of the use of likelihood ratios for ...

  4. Test statistic - Wikipedia

    en.wikipedia.org/wiki/Test_statistic

    Test statistic is a quantity derived from the sample for statistical hypothesis testing. [1] A hypothesis test is typically specified in terms of a test statistic, considered as a numerical summary of a data-set that reduces the data to one value that can be used to perform the hypothesis test. In general, a test statistic is selected or ...

  5. Cognitive pretesting - Wikipedia

    en.wikipedia.org/wiki/Cognitive_pretesting

    Cognitive pretesting. Cognitive pretesting, or cognitive interviewing, is a field research method where data is collected on how the subject answers interview questions. It is the evaluation of a test or questionnaire before it's administered. [1] It allows survey researchers to collect feedback regarding survey responses and is used in ...

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

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

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  7. Regression discontinuity design - Wikipedia

    en.wikipedia.org/wiki/Regression_discontinuity...

    Regression discontinuity design. In statistics, econometrics, political science, epidemiology, and related disciplines, a regression discontinuity design (RDD) is a quasi-experimental pretestposttest design that aims to determine the causal effects of interventions by assigning a cutoff or threshold above or below which an intervention is ...

  8. Kolmogorov–Smirnov test - Wikipedia

    en.wikipedia.org/wiki/Kolmogorov–Smirnov_test

    Illustration of the Kolmogorov–Smirnov statistic. The red line is a model CDF, the blue line is an empirical CDF, and the black arrow is the KS statistic.. Kolmogorov–Smirnov test (K–S test or KS test) is a nonparametric test of the equality of continuous (or discontinuous, see Section 2.2), one-dimensional probability distributions that can be used to test whether a sample came from a ...

  9. Tukey's range test - Wikipedia

    en.wikipedia.org/wiki/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 that have been selected ...