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  2. Statistical proof - Wikipedia

    en.wikipedia.org/wiki/Statistical_proof

    Bayesian statistics are based on a different philosophical approach for proof of inference.The mathematical formula for Bayes's theorem is: [|] = [|] [] []The formula is read as the probability of the parameter (or hypothesis =h, as used in the notation on axioms) “given” the data (or empirical observation), where the horizontal bar refers to "given".

  3. Statistics Indonesia - Wikipedia

    en.wikipedia.org/wiki/Statistics_Indonesia

    Statistics Indonesia (Indonesian: Badan Pusat Statistik, BPS, lit. 'Central Agency of Statistics'), is a non-departmental government institute of Indonesia that is responsible for conducting statistical surveys. Its main customer is the government, but statistical data is also available to the public.

  4. Statistics - Wikipedia

    en.wikipedia.org/wiki/Statistics

    Statistical inference is the process of using data analysis to deduce properties of an underlying probability distribution. [29] Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is sampled from a larger population.

  5. Surrogate data testing - Wikipedia

    en.wikipedia.org/wiki/Surrogate_data_testing

    Surrogate data testing [1] (or the method of surrogate data) is a statistical proof by contradiction technique similar to permutation tests [2] and parametric bootstrapping.It is used to detect non-linearity in a time series. [3]

  6. Lehmann–Scheffé theorem - Wikipedia

    en.wikipedia.org/wiki/Lehmann–Scheffé_theorem

    In statistics, the Lehmann–Scheffé theorem is a prominent statement, tying together the ideas of completeness, sufficiency, uniqueness, and best unbiased estimation. [1] The theorem states that any estimator that is unbiased for a given unknown quantity and that depends on the data only through a complete , sufficient statistic is the unique ...

  7. Law of truly large numbers - Wikipedia

    en.wikipedia.org/wiki/Law_of_truly_large_numbers

    The law of truly large numbers (a statistical adage), attributed to Persi Diaconis and Frederick Mosteller, states that with a large enough number of independent samples, any highly implausible (i.e. unlikely in any single sample, but with constant probability strictly greater than 0 in any sample) result is likely to be observed. [1]

  8. Bias (statistics) - Wikipedia

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

    Statistical bias can have significant real world implications as data is used to inform decision making across a wide variety of processes in society. Data is used to inform lawmaking, industry regulation, corporate marketing and distribution tactics, and institutional policies in organizations and workplaces.

  9. t-statistic - Wikipedia

    en.wikipedia.org/wiki/T-statistic

    Most frequently, t statistics are used in Student's t-tests, a form of statistical hypothesis testing, and in the computation of certain confidence intervals. The key property of the t statistic is that it is a pivotal quantity – while defined in terms of the sample mean, its sampling distribution does not depend on the population parameters, and thus it can be used regardless of what these ...