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  2. Statistical assumption - Wikipedia

    en.wikipedia.org/wiki/Statistical_assumption

    Model-based assumptions. These include the following three types: Distributional assumptions. Where a statistical model involves terms relating to random errors, assumptions may be made about the probability distribution of these errors. [5] In some cases, the distributional assumption relates to the observations themselves. Structural assumptions.

  3. Two-sample hypothesis testing - Wikipedia

    en.wikipedia.org/wiki/Two-sample_hypothesis_testing

    In statistical hypothesis testing, a two-sample test is a test performed on the data of two random samples, each independently obtained from a different given population. The purpose of the test is to determine whether the difference between these two populations is statistically significant .

  4. Statistical model - Wikipedia

    en.wikipedia.org/wiki/Statistical_model

    Informally, a statistical model can be thought of as a statistical assumption (or set of statistical assumptions) with a certain property: that the assumption allows us to calculate the probability of any event. As an example, consider a pair of ordinary six-sided dice. We will study two different statistical assumptions about the dice.

  5. Sample size determination - Wikipedia

    en.wikipedia.org/wiki/Sample_size_determination

    Sample size determination or estimation is the act of choosing the number of observations or replicates to include in a statistical sample.The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample.

  6. Student's t-test - Wikipedia

    en.wikipedia.org/wiki/Student's_t-test

    This assumption is met when the observations used for estimating s 2 come from a normal distribution (and i.i.d. for each group). Z and s are independent. In the t-test comparing the means of two independent samples, the following assumptions should be met: The means of the two populations being compared should follow normal distributions.

  7. Analysis of competing hypotheses - Wikipedia

    en.wikipedia.org/wiki/Analysis_of_competing...

    The resulting hypotheses are converted to a dynamic Bayesian network and value of information analysis is employed to isolate assumptions implicit in the evaluation of paths in, or conclusions of, particular hypotheses. As evidence in the form of observations of states or assumptions is observed, they can become the subject of separate validation.

  8. Models of scientific inquiry - Wikipedia

    en.wikipedia.org/wiki/Models_of_scientific_inquiry

    For example, explanatory power over all existing observations (criterion 3) is satisfied by no one theory at the moment. [ 10 ] Whatever might be the ultimate goals of some scientists, science, as it is currently practiced, depends on multiple overlapping descriptions of the world, each of which has a domain of applicability.

  9. Sensitivity analysis - Wikipedia

    en.wikipedia.org/wiki/Sensitivity_analysis

    Assumptions vs. inferences: In uncertainty and sensitivity analysis there is a crucial trade off between how scrupulous an analyst is in exploring the input assumptions and how wide the resulting inference may be. The point is well illustrated by the econometrician Edward E. Leamer: [58] [59]