I'm trying to evaluate/test how well my data fits a particular distribution.

有几个问题，我被告知使用 scipy.stats.kstest 或 scipy.stats.ks_2samp . 这似乎很简单，给它：(A)数据; (2)分配; (3)拟合参数 . 唯一的问题是我的结果不适合不同的发行版但是从 kstest 的输出中，我不知道我是否可以这样做？

“[SciPy]包含K-S”

“第一个值是测试统计数据，第二个值是p值 . 如果p值小于95(显着性水平为5％)，这意味着你不能拒绝Null-Hypothese那个两个样本分布完全相同 . “

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)

A p_value of pvalue=0.55408436218441004 is saying that the normal and gamma sampling are from the same distirbutions?

现在反对正常分布：

# 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，如果我采用最低的p_值，那么 I would conclude my data came from a gamma distribution even though they are all negative values?

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)

This means at a 5% level of significance, I can reject the null hypothesis that distributions are identical. So I conclude they are different but they clearly aren't? 我是否错误地解释了这个？如果我把它设为单尾，是否会使它越大，它们来自同一分布的可能性越大？