rm(list=ls()) #clears memory
setwd("/rstuff") #sets path


x <- 1:5000 #number of iterations for "for"
h25<-NULL
h50<-NULL
h100<-NULL
b25 <- NULL #creates null set
b50 <- NULL
b100 <- NULL

#generate null distribution
for(i in seq(along=x))
{b25<-c(b25, (mean(runif(25,3,12)))-(mean(runif(25,1,10))))}
#randomly draws 100 continuous numbers
#from 1 to 10, and calculates the mean
#a2 <- sample(1:10, 500, replace=T) would draw 500 discrete observations
#replace=T specifies sampling with replacement; replace=F is without replacement
bet25<-b25/sd(b25)

#generate alternate distribution where means differ by 2
for(i in seq(along=x))
{b50<-c(b50, (mean(runif(50,3,12)))-(mean(runif(50,1,10))))}
bet50<-b50/sd(b50)

#generate alternate distribution where means differ by 4 
for(i in seq(along=x))
{b100<-c(b100, (mean(runif(100,3,12)))-(mean(runif(100,1,10))))}
bet100<-b100/sd(b100)

for(i in seq(along=x))
{h25<-c(h25, (mean(runif(25,1,10)))-(mean(runif(25,1,10))))}

hn25<-h25/sd(h25)

#generate alternate distribution where means differ by 2
for(i in seq(along=x))
{h50<-c(h50, (mean(runif(50,1,10)))-(mean(runif(50,1,10))))}
hn50<-h50/sd(h50)

#generate alternate distribution where means differ by 4 
for(i in seq(along=x))
{h100<-c(h100, (mean(runif(100,1,10)))-(mean(runif(100,1,10))))}
hn100<-h100/sd(h100)

betan <- cbind(hn25,hn50,hn100,bet25,bet50,bet100)

write.csv(betan, file = "betan.csv")