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  2. Oversampling and undersampling in data analysis - Wikipedia

    en.wikipedia.org/wiki/Oversampling_and_under...

    Within statistics, oversampling and undersampling in data analysis are techniques used to adjust the class distribution of a data set (i.e. the ratio between the different classes/categories represented). These terms are used both in statistical sampling, survey design methodology and in machine learning . Oversampling and undersampling are ...

  3. Bootstrapping (statistics) - Wikipedia

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

    Usually the sample drawn has the same sample size as the original data. Then the estimate of original function F can be written as F ^ = F θ ^ {\displaystyle {\hat {F}}=F_{\hat {\theta }}} . This sampling process is repeated many times as for other bootstrap methods.

  4. 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. In practice, the sample size used in a study is usually determined ...

  5. List of statistical software - Wikipedia

    en.wikipedia.org/wiki/List_of_statistical_software

    GAUSS – programming language for statistics. Genedata – software for integration and interpretation of experimental data in the life science R&D. GenStat – general statistics package. GLIM – early package for fitting generalized linear models. GraphPad InStat – very simple with much guidance and explanations.

  6. Statistical hypothesis test - Wikipedia

    en.wikipedia.org/wiki/Statistical_hypothesis_test

    Set up two statistical hypotheses, H1 and H2, and decide about α, β, and sample size before the experiment, based on subjective cost-benefit considerations. These define a rejection region for each hypothesis. 2 Report the exact level of significance (e.g. p = 0.051 or p = 0.049). Do not refer to "accepting" or "rejecting" hypotheses.

  7. Jackknife resampling - Wikipedia

    en.wikipedia.org/wiki/Jackknife_resampling

    Given a sample of size , a jackknife estimator can be built by aggregating the parameter estimates from each subsample of size () obtained by omitting one observation. [ 1 ] The jackknife technique was developed by Maurice Quenouille (1924–1973) from 1949 and refined in 1956.

  8. Chi-squared test - Wikipedia

    en.wikipedia.org/wiki/Chi-squared_test

    Chi-squared distribution, showing χ2 on the x -axis and p -value (right tail probability) on the y -axis. A chi-squared test (also chi-square or χ2 test) is a statistical hypothesis test used in the analysis of contingency tables when the sample sizes are large. In simpler terms, this test is primarily used to examine whether two categorical ...

  9. Data analysis - Wikipedia

    en.wikipedia.org/wiki/Data_analysis

    Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. [ 1] Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science ...