Fisher 檢定

R 中的 A/B 測試

Lauryn Burleigh

Data Scientist

規格

類別資料的非參數方法
- 是否會再吃 Cheese 或 Pepperoni 披薩
樣本 < 1000
至少有 20% 的次數 < 5
使用 Fisher 檢定得到精確 p 值
2x2 次數表

超幾何分配

次數表的概似度

table(Pizza$Topping, Pizza$EatAgain)

              No Yes
  Cheese     140 360
  Pepperoni   80 420

假設

虛無假設：是否再吃披薩與配料組別無關

多人用手拿起披薩切片的照片。

樣本

library(pwr)
pwr.chisq.test(w = 0.1, df = 2, 
               power = 0.80, 
               sig.level = 0.05)

     Chi squared power calculation 
              w = 0.1
              N = 963.4689
             df = 2
      sig.level = 0.05
          power = 0.8
NOTE: N is the number of observations

table(Pizza$Topping, Pizza$EatAgain)

              No Yes
  Cheese     140 360
  Pepperoni   80 420

檢定

freqtbl <- table(Pizza$Topping, 
                 Pizza$Time)
fisher.test(freqtbl)

    Fisher's Exact Test for Count Data
data:  freqtbl
p-value = 0.001809
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.4569013 0.8417208
sample estimates:
odds ratio 
 0.6209987

效果量

freqtbl <- table(Pizza$Topping, 
                 Pizza$Time)
fisher.test(freqtbl)

小：1.5
中：2.5
大：4
Pepperoni 披薩的勝算比是 Cheese 披薩的 0.62 倍

Fisher's Exact Test for Count Data
data:  freqtbl
p-value = 0.001809
alternative hypothesis: true odds 
ratio is not equal to 1
95 percent confidence interval:
 0.4569013 0.8417208
sample estimates:
odds ratio 
 0.6209987

檢定力

library(pwr)
pwr.2p.test(h = 0.62, n = 500, 
            sig.level = 0.002)

    Difference of proportion power 
 calculation for binomial distribution

              h = 0.62
              n = 500
      sig.level = 0.002
          power = 1
    alternative = two.sided

NOTE: same sample sizes

一起來練習吧！

R 中的 A/B 測試