/**
 * SMI-632/SMI-677: Shared statistical utilities for benchmark calculations
 *
 * This module provides consistent statistical calculations across
 * BenchmarkRunner and IndexBenchmark.
 */
/**
 * Calculate percentile value from sorted array using linear interpolation
 *
 * Uses the percentile rank method with linear interpolation between adjacent values.
 * This provides more accurate percentile estimates than simple nearest-rank method.
 *
 * @param sorted - Sorted array of values (ascending order)
 * @param p - Percentile to calculate (0-100)
 * @returns The interpolated percentile value, rounded to 3 decimal places
 *
 * @example
 * percentile([1, 2, 3, 4, 5], 50) // returns 3
 * percentile([1, 2, 3, 4, 5], 95) // returns 4.8 (interpolated)
 */
export declare function percentile(sorted: number[], p: number): number;
/**
 * Calculate mean of an array of values
 *
 * @param values - Array of numeric values
 * @returns The arithmetic mean, or 0 for empty array
 */
export declare function mean(values: number[]): number;
/**
 * Calculate sample standard deviation
 *
 * Uses Bessel's correction (n-1 denominator) for sample standard deviation,
 * which provides an unbiased estimate of population standard deviation.
 *
 * @param values - Array of numeric values
 * @returns The sample standard deviation, or 0 for arrays with less than 2 elements
 */
export declare function sampleStddev(values: number[]): number;
/**
 * Calculate all common statistics from a set of latencies
 *
 * @param latencies - Array of latency measurements
 * @returns Statistical summary including percentiles, mean, stddev, min, max
 */
export interface LatencyStats {
    count: number;
    p50: number;
    p95: number;
    p99: number;
    mean: number;
    stddev: number;
    min: number;
    max: number;
}
export declare function calculateLatencyStats(latencies: number[]): LatencyStats;
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