/**
 * Effect size measures for statistical significance
 */
/**
 * Cohen's d effect size.
 *
 * Standardized mean difference measures the magnitude of difference
 * between two groups in units of standard deviation.
 *
 * Interpretation (Cohen's conventions):
 * - |d| < 0.2: Small effect
 * - 0.2 ≤ |d| < 0.5: Medium effect
 * - 0.5 ≤ |d| < 0.8: Large effect
 * - |d| ≥ 0.8: Very large effect
 *
 * @param group1 - Samples from group 1
 * @param group2 - Samples from group 2
 * @returns Effect size and interpretation category
 *
 * @example
 * ```typescript
 * const treatment = [8, 9, 7, 8, 10];
 * const control = [5, 6, 5, 6, 7];
 * const result = cohensD(treatment, control);
 * console.log(result.effectSize); // e.g., 1.8 (very large effect)
 * console.log(result.interpretation); // 'very-large'
 * ```
 */
export declare const cohensD: (group1: number[], group2: number[]) => {
    effectSize: number;
    interpretation: "negligible" | "small" | "medium" | "large" | "very-large";
    magnitude: number;
};
/**
 * Cliff's delta (non-parametric effect size).
 *
 * Measures dominance between two groups without assuming normal distribution.
 * Based on probability that a randomly selected value from group1 exceeds
 * a randomly selected value from group2.
 *
 * Interpretation:
 * - |δ| < 0.147: Negligible
 * - 0.147 ≤ |δ| < 0.33: Small
 * - 0.33 ≤ |δ| < 0.474: Medium
 * - |δ| ≥ 0.474: Large
 *
 * @param group1 - Samples from group 1
 * @param group2 - Samples from group 2
 * @returns Effect size and interpretation
 *
 * @example
 * ```typescript
 * const treatment = [8, 9, 7, 8, 10];
 * const control = [5, 6, 5, 6, 7];
 * const result = cliffsDelta(treatment, control);
 * console.log(result.effectSize); // e.g., 0.92 (large effect)
 * console.log(result.interpretation); // 'large'
 * console.log(result.probability);  // e.g., 0.96 (96% chance treatment > control)
 * ```
 */
export declare const cliffsDelta: (group1: number[], group2: number[]) => {
    effectSize: number;
    interpretation: "negligible" | "small" | "medium" | "large";
    probability: number;
    magnitude: number;
};
/**
 * Glass's delta (alternative to Cohen's d).
 *
 * Uses standard deviation of control group only instead of pooled SD.
 * Useful when treatment group variance is expected to differ from control.
 *
 * @param treatment - Samples from treatment group
 * @param control - Samples from control group (used for SD normalization)
 * @returns Effect size and interpretation
 *
 * @example
 * ```typescript
 * const treatment = [8, 9, 7, 8, 10];
 * const control = [5, 6, 5, 6, 7];
 * const result = glassDelta(treatment, control);
 * console.log(result.effectSize); // e.g., 1.8
 * ```
 */
export declare const glassDelta: (treatment: number[], control: number[]) => {
    effectSize: number;
    interpretation: "negligible" | "small" | "medium" | "large" | "very-large";
};
/**
 * Rank-biserial correlation (non-parametric effect size).
 *
 * Based on Mann-Whitney U test. Measures relationship between group
 * membership and rank ordering.
 *
 * Interpretation similar to Cliff's delta.
 *
 * @param group1 - Samples from group 1
 * @param group2 - Samples from group 2
 * @returns Effect size, correlation coefficient, and interpretation
 *
 * @example
 * ```typescript
 * const group1 = [8, 9, 7, 8, 10];
 * const group2 = [5, 6, 5, 6, 7];
 * const result = rankBiserialCorrelation(group1, group2);
 * console.log(result.correlation); // e.g., 0.85 (strong positive correlation)
 * console.log(result.effectSize);  // e.g., 0.70 (large effect)
 * ```
 */
export declare const rankBiserialCorrelation: (group1: number[], group2: number[]) => {
    effectSize: number;
    correlation: number;
    interpretation: "negligible" | "small" | "medium" | "large";
};
//# sourceMappingURL=effect-size.d.ts.map