/**
 * Overlap
 * Generally called the overlapping coefficient (OVL). Cohen’s d can be converted to OVL using the following formula (Reiser and Faraggi, 1999)
 *
 * OVL=2Φ(−∣δ∣/2)
 * where Φ is the cumulative distribution function of the standard normal distribution, and δ the population Cohen’s d.
 */
export declare const calcGaussOverlap: (cohensD: number) => number;
/**
 * Calculates the probability of superiority
 *
 * This is effect size with many names: common language effect size (CL),
 * Area under the receiver operating characteristics (AUC) or just A for its non-parametric version (Ruscio & Mullen, 2012).
 * It is meant to be more intuitive for persons without any training in statistics.
 * The effect size gives the probability that a person picked at random from the treatment group
 * will have a higher score than a person picked at random from the control group.
 * Cohen’s d can be converted CL using the following formula (Ruscio, 2008)
 * CL=Φ(δ/√2)
 * where Φ is the cumulative distribution function of the standard normal distribution, and δ the population Cohen’s d.
 */
export declare const calcCL: (cohensD: number) => number;
export declare const calcChangeProbability: (probabilityOfSuperiority: number) => number;
/**
 * Cohen’s U₃
 * Cohen (1977) defined U3 as a measure of non-overlap, where “we take the percentage of the A population
 * which the upper half of the cases of the Β population exceeds”.
 * Cohen’s d can be converted to Cohen’s U3 using the following formula
 * U₃=Φ(δ)
 * where Φ is the cumulative distribution function of the standard normal distribution, and δ the population Cohen’s d.
 */
export declare const calcU3: (cohensD: number) => number;
/**
 * Cohen's d is a way to determine the effect size of a statistical test.
 * The resulting value is the standardized mean difference,
 * δ=(μ2−μ1)/σ
 * where δ is the population parameter of Cohen’s d. Where it is assumed that σ1=σ2=σ,
 * i.e., homogeneous population variances. And μi is the mean of the respective population.
 */
export declare const calcCohensD: ({ data1, data2, n1, n2, mean1, mean2, pooledStDev, correctForSmallSampleSizeWhenBelow, }: {
    data1: number[];
    data2: number[];
    n1?: number | undefined;
    n2?: number | undefined;
    mean1?: number | undefined;
    mean2?: number | undefined;
    pooledStDev?: number | undefined;
    /**
     * The constants 3 and 2.25 in the correction formula are related to the degrees of freedom of the t-distribution.
     * It is worth noting that these constants are not universally accepted, and there is some debate about their appropriate use.
     * It is always a good idea to carefully consider the assumptions underlying any correction that you apply,
     * and to consider whether it is appropriate for your specific research context.
     * @default 50
     */
    correctForSmallSampleSizeWhenBelow?: number | undefined;
}) => number;
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