在这种情况下，Kolmogorov-Smirnov 统计量可能会对您有所帮助。
下面是一个使用 Kolmogorov-Smirnov 统计的实现，该函数返回相似的概率。

#include <math.h>
#define EPS1 0.001
#define EPS2 1.0e-8

float kstest(float alam) {
    int j;
    float a2, fac = 2.0, sum = 0.0, term, termbf = 0.0;
    a2 = -2.0 * alam * alam;
    for (j = 1; j <= 100; j++) {
    term = fac * exp(a2 * j * j);
    sum += term;
    if (fabs(term) <= EPS1 * termbf || fabs(term) <= EPS2 * sum)
        return sum;
    fac = -fac;
    termbf = fabs(term);
    }
    return 1.0;
}

void checkSameDist(float data1[], unsigned long n1, float data2[],
          unsigned long n2, float *d, float *prob) {
    float           kstest(float alam);
    void            sort(unsigned long n, float arr[]);
    unsigned long   j1 = 1,
    j2 = 1;
    float d1, d2, dt, en1, en2, en, fn1 = 0.0, fn2 = 0.0;
    sort(n1, data1);
    sort(n2, data2);
    en1 = n1;
    en2 = n2;
    *d = 0.0;
    while (j1 <= n1 && j2 <= n2) {
        if ((d1 = data1[j1]) <= (d2 = data2[j2]))
            fn1 = j1++ / en1;
        if (d2 <= d1)
            fn2 = j2++ / en2;
        if ((dt = fabs(fn2 - fn1)) > *d)
            *d = dt;
    }
    en = sqrt(en1 * en2 / (en1 + en2));
    *prob = kstest((en + 0.12 + 0.11 / en) * (*d));
}

还要检查，以下函数检查特定分布是否正常，您可以对其进行一些修改（这将使您对统计数据有更多的直觉以及如何从头开始实现它
（https://walteis.wordpress.com/2012 /04/26/a-kolmogorov-smirnov-implementation/ )

public bool IsNormal
{
    get
    {
        // This method uses the Kolmogorov-Smirnov test to determine a normal distribution.
        // The level of significance (alpha) used is .05, and the critical values used are from Table 1 of: 
        // The Kolmogorov-Smirnov Test for Goodness of Fit
        // Frank J. Massey, Jr.
        // Journal of the American Statistical Association
        // Vol. 46, No. 253 (Mar., 1951) (pp. 68-78)

        if (DataSet.Count == 0)
            return false;

        List<double> vals = DataSet.Values.ToList();
        Accumulator acc = new Accumulator(vals.ToArray());
        double dmax = double.MinValue;
        double cv = 0;

        MathNet.Numerics.Distributions.NormalDistribution test = new MathNet.Numerics.Distributions.NormalDistribution(acc.Mean, acc.Sigma);

        // the 0 entry is to force the list to be a base 1 index table.
        List<double> cvTable = new List<double>() { 0, .975, .842, .708, .624, .565,
                                                .521, .486, .457, .432, .410,
                                                .391, .375, .361, .349, .338,
                                                .328, .318, .309, .301, .294};

        test.EstimateDistributionParameters(DataSet.Values.ToArray());
        vals.Sort();

        for (int i = 0; i < vals.Count; i++)
        {
            double dr = Math.Abs(((i + 1) / (double)vals.Count) - test.CumulativeDistribution(vals[i]));
            double dl = Math.Abs(test.CumulativeDistribution(vals[i]) - (i / (double)vals.Count));
            dmax = Math.Max(dmax, Math.Max(dl, dr));
        }

        // get critical value and compare to d(N)
        if (vals.Count <= 10)
            cv = cvTable[vals.Count];
        else if (vals.Count > 10)
            cv = 1.36 / Math.Sqrt(vals.Count);

        return (dmax < cv);
    }
}

祝你好运