所以我已经粘贴了我的完整代码供你参考，我想知道这里的ppf和cdf有什么用？你能解释一下吗？我做了一些研究，发现 ppf（百分点函数）是 CDF（计算分布函数）的倒数，如果它们真的是，如果我将 ppf 和 cdf 分别替换为 1/cdf 和 1/ppf，这段代码不应该工作吗?
请向我解释一下，两者之间的区别。以及如何以及何时使用 which
顺便说一句，这是假设检验。对这么多评论感到抱歉，只是习惯于解释一切以供我将来参考。（如果我的任何评论对此有误，请指出我）
ball_bearing_radius = [2.99, 2.99, 2.70, 2.92, 2.88, 2.92, 2.82, 2.83, 3.06, 2.85]
import numpy as np
from math import sqrt
from scipy.stats import norm
# h1 : u != U_0
# h0 : u = u_0
#case study : ball bearing example, claim is that radius = 3, do hypothesis testing
mu_0 = 3
sigma = 0.1
#collect sample
sample = ball_bearing_radius
#compute mean
mean = np.mean(sample)
#compute n
n = len(sample)
#compute test statistic
z = (mean - mu_0) /(sigma/sqrt(n))
#set alpha
a = 0.01
#-------------------------
#calculate the z_a/2, by using percent point function of the norm of scipy
#ppf = percent point function, inverse of CDF(comulative distribution function)
#also, CDF = pr(X<=x), i.e., probability to the left of the distribution
z_critical = norm.ppf(1-a/2) #this returns a value for which the probab to the left is 0.975
p_value = 2*(1 - norm.cdf(np.abs(z)))
p_value = float("{:.4f}".format(p_value))
print('z : ',z)
print('\nz_critical :', z_critical)
print('\nmean :', mean, "\n\n")
#test the hypothesis
if (np.abs(z) > z_critical):
print("\nREJECT THE NULL HYPOTHESIS : \n p-value = ", p_value, "\n Alpha = ", a )
else:
print("CANNOT REJECT THE NULL HYPOTHESIS. NOT ENOUGH EVIDENCE TO REJECT IT: \n p-value = ", p_value, "\n Alpha = ", a )
