我正在尝试评估/测试我的数据是否符合特定分布。

关于它有几个问题，我被告知要么使用scipy.stats.kstest，要么使用scipy.stats.ks_2samp。这似乎很简单，给出：(A)数据；(2)分布；(3)拟合参数。唯一的问题是我的结果毫无意义？我想测试我的数据的“好”度，它适合不同的分布，但是从kstest的输出来看，我不知道我是否能做到这一点？"[SciPy] contains K-S""first value is the test statistics, and second value is the p-value. if the p-value is less than 95 (for a level of significance of 5%), this means that you cannot reject the Null-Hypothese that the two sample distributions are identical."np.random.seed(2)

# Sample from a normal distribution w/ mu: -50 and sigma=1

x = np.random.normal(loc=-50, scale=1, size=100)

x

#array([-50.41675785, -50.05626683, -52.1361961 , -48.35972919,

# -51.79343559, -50.84174737, -49.49711858, -51.24528809,

# -51.05795222, -50.90900761, -49.44854596, -47.70779199,

# ...

# -50.46200535, -49.64911151, -49.61813377, -49.43372456,

# -49.79579202, -48.59330376, -51.7379595 , -48.95917605,

# -49.61952803, -50.21713527, -48.8264685 , -52.34360319])

# Try against a Gamma Distribution

distribution = "gamma"

distr = getattr(stats, distribution)

params = distr.fit(x)

stats.kstest(x,distribution,args=params)

KstestResult(statistic=0.078494356486987549, pvalue=0.55408436218441004)

p{}的p值表示normal和gamma采样来自相同的分布？

现在与正态分布相反：# Try against a Normal Distribution

distribution = "norm"

distr = getattr(stats, distribution)

params = distr.fit(x)

stats.kstest(x,distribution,args=params)

KstestResult(statistic=0.070447707170256002, pvalue=0.70801104133244541)

根据这个，如果我取最小的p_值，那么我会得出结论，我的数据来自一个gamma分布，即使它们都是负值？np.random.seed(0)

distr = getattr(stats, "norm")

x = distr.rvs(loc=0, scale=1, size=50)

params = distr.fit(x)

stats.kstest(x,"norm",args=params, N=1000)

KstestResult(statistic=0.058435890774587329, pvalue=0.99558592119926814)

这意味着在5%的显著性水平上，我可以拒绝分布相同的零假设。所以我得出结论，它们是不同的，但显然不是？我的解释有误吗？如果我把它设为单尾分布，那么它的值越大，它们来自同一分布的可能性就越大吗？