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  2. p-value - Wikipedia

    en.wikipedia.org/wiki/P-value

    In his highly influential book Statistical Methods for Research Workers (1925), Fisher proposed the level p = 0.05, or a 1 in 20 chance of being exceeded by chance, as a limit for statistical significance, and applied this to a normal distribution (as a two-tailed test), thus yielding the rule of two standard deviations (on a normal ...

  3. Statistical significance - Wikipedia

    en.wikipedia.org/wiki/Statistical_significance

    To determine whether a result is statistically significant, a researcher calculates a p-value, which is the probability of observing an effect of the same magnitude or more extreme given that the null hypothesis is true. [5] [12] The null hypothesis is rejected if the p-value is less than (or equal to) a predetermined level, .

  4. Misuse of p-values - Wikipedia

    en.wikipedia.org/wiki/Misuse_of_p-values

    This means that the p-value is a statement about the relation of the data to that hypothesis. [2] The 0.05 significance level is merely a convention. [3] [5] The 0.05 significance level (alpha level) is often used as the boundary between a statistically significant and a statistically non-significant p-value. However, this does not imply that ...

  5. Levene's test - Wikipedia

    en.wikipedia.org/wiki/Levene's_test

    The test statistic is approximately F-distributed with and degrees of freedom, and hence is the significance of the outcome of tested against (;,) where is a quantile of the F-distribution, with and degrees of freedom, and is the chosen level of significance (usually 0.05 or 0.01).

  6. Chi-squared distribution - Wikipedia

    en.wikipedia.org/wiki/Chi-squared_distribution

    These values can be calculated evaluating the quantile function (also known as "inverse CDF" or "ICDF") of the chi-squared distribution; [24] e. g., the χ 2 ICDF for p = 0.05 and df = 7 yields 2.1673 ≈ 2.17 as in the table above, noticing that 1 – p is the p-value from the table.

  7. Null hypothesis - Wikipedia

    en.wikipedia.org/wiki/Null_hypothesis

    Also measurements will never indicate a non-zero probability of exactly zero difference.) So failure of an exclusion of a null hypothesis amounts to a "don't know" at the specified confidence level; it does not immediately imply null somehow, as the data may already show a (less strong) indication for a non-null.

  8. Omnibus test - Wikipedia

    en.wikipedia.org/wiki/Omnibus_test

    The last line describes the omnibus F test for model fit. The interpretation is that the null hypothesis is rejected (P = 0.02692<0.05, α=0.05). So Either β1 or β2 appears to be non-zero (or perhaps both). Note that the conclusion from Coefficients: table is that only β1 is significant (P-Value shown on Pr(>|t|) column is 4.37e-05 << 0.001).

  9. 97.5th percentile point - Wikipedia

    en.wikipedia.org/wiki/97.5th_percentile_point

    "The value for which P = .05, or 1 in 20, is 1.96 or nearly 2; it is convenient to take this point as a limit in judging whether a deviation is to be considered significant or not." [11] In Table 1 of the same work, he gave the more precise value 1.959964. [12] In 1970, the value truncated to 20 decimal places was calculated to be