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p. -value. In null-hypothesis significance testing, the -value[note 1] is the probability of obtaining test results at least as extreme as the result actually observed, under the assumption that the null hypothesis is correct. [2][3] A very small p -value means that such an extreme observed outcome would be very unlikely under the null hypothesis.
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).
In probability theory and statistics, the chi-squared distribution (also chi-square or -distribution) with degrees of freedom is the distribution of a sum of the squares of independent standard normal random variables. The chi-squared distribution is a special case of the gamma distribution and the univariate Wishart distribution.
Statistical significance. In statistical hypothesis testing, [1][2] a result has statistical significance when a result at least as "extreme" would be very infrequent if the null hypothesis were true. [3] More precisely, a study's defined significance level, denoted by , is the probability of the study rejecting the null hypothesis, given that ...
Direct interpretation of the harmonic mean p-value. The weighted harmonic mean of p -values is defined as where are weights that must sum to one, i.e. . Equal weights may be chosen, in which case . In general, interpreting the HMP directly as a p -value is anti-conservative, meaning that the false positive rate is higher than expected.
The partition coefficient, abbreviated P, is defined as a particular ratio of the concentrations of a solute between the two solvents (a biphase of liquid phases), specifically for un- ionized solutes, and the logarithm of the ratio is thus log P. [10]: 275ff When one of the solvents is water and the other is a non-polar solvent, then the log P ...
The Bonferroni correction compensates for that increase by testing each individual hypothesis at a significance level of , where is the desired overall alpha level and is the number of hypotheses. [4] For example, if a trial is testing hypotheses with a desired overall , then the Bonferroni correction would test each individual hypothesis at .
Omnibus test. Omnibus tests are a kind of statistical test. They test whether the explained variance in a set of data is significantly greater than the unexplained variance, overall. One example is the F-test in the analysis of variance. There can be legitimate significant effects within a model even if the omnibus test is not significant.