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  2. Statistical hypothesis test - Wikipedia

    en.wikipedia.org/wiki/Statistical_hypothesis_test

    An example of Neyman–Pearson hypothesis testing (or null hypothesis statistical significance testing) can be made by a change to the radioactive suitcase example. If the "suitcase" is actually a shielded container for the transportation of radioactive material, then a test might be used to select among three hypotheses: no radioactive source ...

  3. Multiple comparisons problem - Wikipedia

    en.wikipedia.org/wiki/Multiple_comparisons_problem

    Production of a small p-value by multiple testing. 30 samples of 10 dots of random color (blue or red) are observed. On each sample, a two-tailed binomial test of the null hypothesis that blue and red are equally probable is performed. The first row shows the possible p-values as a function of the number of blue and red dots in the sample.

  4. Design of experiments - Wikipedia

    en.wikipedia.org/wiki/Design_of_experiments

    The independent variable of a study often has many levels or different groups. In a true experiment, researchers can have an experimental group, which is where their intervention testing the hypothesis is implemented, and a control group, which has all the same element as the experimental group, without the interventional element.

  5. Testing hypotheses suggested by the data - Wikipedia

    en.wikipedia.org/wiki/Testing_hypotheses...

    In statistics, hypotheses suggested by a given dataset, when tested with the same dataset that suggested them, are likely to be accepted even when they are not true.This is because circular reasoning (double dipping) would be involved: something seems true in the limited data set; therefore we hypothesize that it is true in general; therefore we wrongly test it on the same, limited data set ...

  6. Power (statistics) - Wikipedia

    en.wikipedia.org/wiki/Power_(statistics)

    In frequentist statistics, power is a measure of the ability of an experimental design and hypothesis testing setup to detect a particular effect if it is truly present. In typical use, it is a function of the test used (including the desired level of statistical significance ), the assumed distribution of the test (for example, the degree of ...

  7. Type I and type II errors - Wikipedia

    en.wikipedia.org/wiki/Type_I_and_type_II_errors

    [11] As a consequence of this, in experimental science the null hypothesis is generally a statement that a particular treatment has no effect; in observational science, it is that there is no difference between the value of a particular measured variable, and that of an experimental prediction. [citation needed]

  8. Hypothetico-deductive model - Wikipedia

    en.wikipedia.org/wiki/Hypothetico-deductive_model

    Such evidence is called a falsification of the hypothesis. However, under the theory of confirmation holism it is always possible to save a given hypothesis from falsification. This is so because any falsifying observation is embedded in a theoretical background, which can be modified in order to save the hypothesis.

  9. The Design of Experiments - Wikipedia

    en.wikipedia.org/wiki/The_Design_of_Experiments

    The Design of Experiments is a 1935 book by the English statistician Ronald Fisher about the design of experiments and is considered a foundational work in experimental design. [ 1 ] [ 2 ] [ 3 ] Among other contributions, the book introduced the concept of the null hypothesis in the context of the lady tasting tea experiment. [ 4 ]