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power.prop.test 功能不可互换

[英]power.prop.test function not interchangeable

I am using the power.prop.test function in R.我在 R 中使用power.prop.test函数。

I am doing an A/B Test where I am determining the lift from a minimum amount of impressions per group in order for the A/B Test to be significant.我正在做一个 A/B 测试,我正在确定每个组的最小展示量的提升,以使 A/B 测试显着。

When I run the function like below, I get the second proportion (p2) to be 0.0001870215 with n being 2,571,429 per group:当我运行如下函数时,我得到第二个比例 (p2) 为 0.0001870215,每组 n 为 2,571,429:

original_conversion_rate<-0.00009

power.prop.test(n=2571429,
  p1=original_conversion_rate, 
            power=0.8, 
            sig.level=0.05)

My answer is this:我的回答是这样的:

     Two-sample comparison of proportions power calculation 

          n = 2571429
         p1 = 9e-05
         p2 = 0.0001870215
  sig.level = 0.05
      power = 0.8
alternative = two.sided

NOTE: n is number in *each* group

When I rerun this with my answer for p2 (0.0001870215) to solve for n, a different n comes up (230,952.6):当我用 p2 (0.0001870215) 的答案重新运行它来求解 n 时,会出现一个不同的 n (230,952.6):

original_conversion_rate<-0.00009

power.prop.test(
  p1=original_conversion_rate, 
  p2=0.0001870215,
            power=0.8, 
            sig.level=0.05)

My n changes to this:我的 n 更改为:

 Two-sample comparison of proportions power calculation 

          n = 230952.6
         p1 = 9e-05
         p2 = 0.0001870215
  sig.level = 0.05
      power = 0.8
alternative = two.sided

Why would n change in this case?为什么在这种情况下 n 会改变?

Your frequencies are so small that miniscule changes will have a huge impact - especially with the default tolerance level used in power.prop.test .您的频率是如此之小,以至于微小的变化都会产生巨大的影响——尤其是在power.prop.test使用的默认容差级别。

If you take one iteration more you get如果你再进行一次迭代,你会得到

power.prop.test(n=230952.5,
  p1=original_conversion_rate, 
            power=0.8, 
            sig.level=0.05)

     Two-sample comparison of proportions power calculation 

              n = 230952.5
             p1 = 9e-05
             p2 = 0.0001870215
      sig.level = 0.05
          power = 0.8
    alternative = two.sided

NOTE: n is number in *each* group

which is the same value for p2 as before.这与之前的p2值相同。 Now, if we go back to your first calculation and lower the tolerance we get现在,如果我们回到你的第一个计算并降低我们得到的容差

power.prop.test(n=2571429,
  p1=original_conversion_rate, 
            power=0.8, 
            sig.level=0.05, tol=.Machine$double.eps^.8) 

     Two-sample comparison of proportions power calculation 

              n = 2571429
             p1 = 9e-05
             p2 = 0.0001150142
      sig.level = 0.05
          power = 0.8
    alternative = two.sided

NOTE: n is number in *each* group

You can see that p2 changed.您可以看到p2发生了变化。 If we insert the updated value for p2 and solve for n we get如果我们插入p2的更新值并求解n我们得到

power.prop.test(
  p1=original_conversion_rate, 
  p2=0.0001150142,
            power=0.8, 
            sig.level=0.05)

     Two-sample comparison of proportions power calculation 

              n = 2571429
             p1 = 9e-05
             p2 = 0.0001150142
      sig.level = 0.05
          power = 0.8
    alternative = two.sided

NOTE: n is number in *each* group

and you get the same n .你得到相同的n

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