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  2. Pearson's chi-squared test - Wikipedia

    en.wikipedia.org/wiki/Pearson's_chi-squared_test

    For the test of independence, also known as the test of homogeneity, a chi-squared probability of less than or equal to 0.05 (or the chi-squared statistic being at or larger than the 0.05 critical point) is commonly interpreted by applied workers as justification for rejecting the null hypothesis that the row variable is independent of the ...

  3. Chi-squared test - Wikipedia

    en.wikipedia.org/wiki/Chi-squared_test

    A chi-squared test (also chi-square or χ 2 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 variables ( two dimensions of the contingency table ) are independent in influencing the test statistic ...

  4. Yates's correction for continuity - Wikipedia

    en.wikipedia.org/wiki/Yates's_correction_for...

    In statistics, Yates's correction for continuity (or Yates's chi-squared test) is used in certain situations when testing for independence in a contingency table.

  5. Chi-squared distribution - Wikipedia

    en.wikipedia.org/wiki/Chi-squared_distribution

    Chi-squared test of independence in contingency tables; Chi-squared test of goodness of fit of observed data to hypothetical distributions; Likelihood-ratio test for nested models; Log-rank test in survival analysis; Cochran–Mantel–Haenszel test for stratified contingency tables; Wald test; Score test

  6. Contingency table - Wikipedia

    en.wikipedia.org/wiki/Contingency_table

    The significance of the difference between the two proportions can be assessed with a variety of statistical tests including Pearson's chi-squared test, the G-test, Fisher's exact test, Boschloo's test, and Barnard's test, provided the entries in the table represent individuals randomly sampled from the population about which conclusions are to ...

  7. Independence (probability theory) - Wikipedia

    en.wikipedia.org/wiki/Independence_(probability...

    Independence is a fundamental notion in probability theory, as in statistics and the theory of stochastic processes.Two events are independent, statistically independent, or stochastically independent [1] if, informally speaking, the occurrence of one does not affect the probability of occurrence of the other or, equivalently, does not affect the odds.

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  9. Basu's theorem - Wikipedia

    en.wikipedia.org/wiki/Basu's_theorem

    Let X 1, X 2, ..., X n be independent, identically distributed normal random variables with mean μ and variance σ 2.. Then with respect to the parameter μ, one can show that ^ =, the sample mean, is a complete and sufficient statistic – it is all the information one can derive to estimate μ, and no more – and