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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.
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 ...
If the resulting p-value of Levene's test is less than some significance level (typically 0.05), the obtained differences in sample variances are unlikely to have occurred based on random sampling from a population with equal variances. Thus, the null hypothesis of equal variances is rejected and it is concluded that there is a difference ...
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 .
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.
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 ...
t. e. Bayesian inference (/ ˈbeɪziən / BAY-zee-ən or / ˈbeɪʒən / BAY-zhən) [ 1 ] is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Fundamentally, Bayesian inference uses prior knowledge, in the form of a prior distribution ...
P (A), the prior, is the initial degree of belief in A. P (A | B), the posterior, is the degree of belief after incorporating news that B is true. the quotient P(B | A) / P(B) represents the support B provides for A. For more on the application of Bayes' theorem under the Bayesian interpretation of probability, see Bayesian inference.