commons-statistics-descriptive/src/main/java/org/apache/commons/statistics/descriptive/IntVariance.java [222:241]:
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        if (n == 0) {
            return Double.NaN;
        }
        // Avoid a divide by zero
        if (n == 1) {
            return 0;
        }
        // Sum-of-squared deviations: sum(x^2) - sum(x)^2 / n
        // Sum-of-squared deviations precursor: n * sum(x^2) - sum(x)^2
        // The precursor is computed in integer precision.
        // The divide uses double precision.
        // This ensures we avoid cancellation in the difference and use a fast divide.
        // The result is limited to by the rounding in the double computation.
        final double diff = computeSSDevN(sumSq, sum, n);
        final long n0 = biased ? n : n - 1;
        final double v = diff / IntMath.unsignedMultiplyToDouble(n, n0);
        if (std) {
            return Math.sqrt(v);
        }
        return v;
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commons-statistics-descriptive/src/main/java/org/apache/commons/statistics/descriptive/LongVariance.java [199:218]:
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        if (n == 0) {
            return Double.NaN;
        }
        // Avoid a divide by zero
        if (n == 1) {
            return 0;
        }
        // Sum-of-squared deviations: sum(x^2) - sum(x)^2 / n
        // Sum-of-squared deviations precursor: n * sum(x^2) - sum(x)^2
        // The precursor is computed in integer precision.
        // The divide uses double precision.
        // This ensures we avoid cancellation in the difference and use a fast divide.
        // The result is limited to by the rounding in the double computation.
        final double diff = computeSSDevN(sumSq, sum, n);
        final long n0 = biased ? n : n - 1;
        final double v = diff / IntMath.unsignedMultiplyToDouble(n, n0);
        if (std) {
            return Math.sqrt(v);
        }
        return v;
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