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--- summary_of_hypothesis_testing [2024/12/02 08:56] – hkimscil
+++ summary_of_hypothesis_testing [2025/09/13 21:45] (current) – [Hypothesis testing] hkimscil
@@ Line 1: / Line 1: @@
+====== Hypothesis testing ======
+see also [[:types of error]]
+====== Basic ======
+see first [[:sampling distribution and z-test]]
+====== Hypothesis testing, exp ======
+<code>
+rm(list=ls())
+rnorm2 <- function(n,mean,sd){
+  mean+sd*scale(rnorm(n))
+}
+n.p <- 10000
+m.p <- 100
+sd.p <- 10
+p1 <- rnorm2(n.p, m.p, sd.p)
+m.p1 <- mean(p1)
+sd.p1 <- sd(p1)
+p2 <- rnorm2(n.p, m.p+10, sd.p)
+m.p2 <- mean(p2)
+sd.p2 <- sd(p2)
+n.s <- 100
+se.z1 <- c(sqrt(var(p1)/n.s))
+se.z2 <- c(sqrt(var(p2)/n.s))
+x.p1 <- seq(mean(p1)-5*se.z1,
+                mean(p2)+5*se.z1,
+                length.out = 500)
+x.p2 <- seq(mean(p2)-5*se.z1,
+            mean(p2)+5*se.z1,
+            length.out = 500)
+# Calculate the probability
+# density for a normal distribution
+y.p1 <- dnorm(x.p1, mean(p1), se.z1)
+y.p2 <- dnorm(x.p2, mean(p2), se.z2)
+# Plot the theoretical PDF
+plot(x.p1, y.p1, type = "l",
+     lwd=3,
+     main = "Sample means from p1 and p2 (imaginary)",
+     xlab = "Value", ylab = "Density")
+lines(x.p2, y.p2, lty=2, lwd=3, add=T)
+m.p1 <- mean(p1)
+se1 <- c(m.p1-se.z1, m.p1+se.z1)
+se2 <- c(m.p1-2*se.z1, m.p1+2*se.z1)
+se3 <- c(m.p1-3*se.z1, m.p1+3*se.z1)
+abline(v=c(m.p1,se1,se2,se3),
+       col=c('black', 'orange', 'orange',
+             'green', 'green',
+             'blue', 'blue'),
+       lwd=1)
+treated.s <- sample(p2, n.s)
+m.treated.s <- mean(treated.s)
+abline(v=m.treated.s, col='red', lwd=2)
+se.z1
+diff <- m.treated.s-mean(p1)
+diff/se.z1
+# usual way - using sample's variance
+# instead of p1's variance to get
+# standard error value
+se.s <- sqrt(var(treated.s)/n.s)
+se.s
+diff/se.s
+pt(diff/se.s, df=n.s-1, lower.tail = F) * 2
+t.test(treated.s, mu=m.p1, var.equal = T)
+</code>
+===== output =====
+<code>
+>
+>
+> rm(list=ls())
+>
+> rnorm2 <- function(n,mean,sd){
++   mean+sd*scale(rnorm(n))
++ }
+>
+> n.p <- 10000
+> m.p <- 100
+> sd.p <- 10
+> p1 <- rnorm2(n.p, m.p, sd.p)
+> m.p1 <- mean(p1)
+> sd.p1 <- sd(p1)
+>
+> p2 <- rnorm2(n.p, m.p+10, sd.p)
+> m.p2 <- mean(p2)
+> sd.p2 <- sd(p2)
+>
+> n.s <- 100
+> se.z1 <- c(sqrt(var(p1)/n.s))
+> se.z2 <- c(sqrt(var(p2)/n.s))
+>
+> x.p1 <- seq(mean(p1)-5*se.z1,
++                 mean(p2)+5*se.z1,
++                 length.out = 500)
+> x.p2 <- seq(mean(p2)-5*se.z1,
++             mean(p2)+5*se.z1,
++             length.out = 500)
+>
+> # Calculate the probability
+> # density for a normal distribution
+> y.p1 <- dnorm(x.p1, mean(p1), se.z1)
+> y.p2 <- dnorm(x.p2, mean(p2), se.z2)
+>
+> # Plot the theoretical PDF
+> plot(x.p1, y.p1, type = "l",
++      lwd=3,
++      main = "Sample means from p1 and p2 (imaginary)",
++      xlab = "Value", ylab = "Density")
+> lines(x.p2, y.p2, lty=2, lwd=3)
+>
+>
+> m.p1 <- mean(p1)
+> se1 <- c(m.p1-se.z1, m.p1+se.z1)
+> se2 <- c(m.p1-2*se.z1, m.p1+2*se.z1)
+> se3 <- c(m.p1-3*se.z1, m.p1+3*se.z1)
+> abline(v=c(m.p1,se1,se2,se3),
++        col=c('black', 'orange', 'orange',
++              'green', 'green',
++              'blue', 'blue'),
++        lwd=1)
+>
+> treated.s <- sample(p2, n.s)
+> m.treated.s <- mean(treated.s)
+> abline(v=m.treated.s, col='red', lwd=2)
+>
+</code>
+{{:pasted:20250913-184943.png}}
+<code>
+> se.z1
+[1] 1
+>
+> diff <- m.treated.s-mean(p1)
+> diff/se.z1
+[1] 9.057418
+>
+> # usual way - using sample's variance
+> # instead of p1's variance to get
+> # standard error value
+> se.s <- sqrt(var(treated.s)/n.s)
+> se.s
+[1] 1.015243
+> diff/se.s
+[1] 8.921425
+>
+> pt(diff/se.s, df=n.s-1, lower.tail = F) * 2
+[1] 2.455388e-14
+> t.test(treated.s, mu=m.p1, var.equal = T)
+	One Sample t-test
+data:  treated.s
+t = 8.9214, df = 99, p-value = 2.455e-14
+alternative hypothesis: true mean is not equal to 100
+percent confidence interval:
+.0430 111.0719
+sample estimates:
+mean of x
+.0574
+>
+</code>
+====== se value and sample size ======
 <code>
 n.ajstu <- 100000