#Part 1

#Attempt to Replicate with n=500 per group, mu=19(church),20(nochurch),
#sigma=5 (both groups)
d<-read.csv(file.choose())
t.test(mot_apol ~ group, data=d)

#Replication
tapply(d$mot_apol,d$group,mean)
tapply(d$mot_apol,d$group,sd)
mta<-c(rnorm(500,mean=19.29,sd=4.1),
       rnorm(500,mean=20.12,sd=4.1))
t.test(mta~d$group)
#probably significant as well

#Replication for approx 
#n = 50 per group
d2<-sample_n(d,100)
table(d2$group)
t.test(mot_apol ~ group, data=d2)
#Run these lines over and over again
#to get a sense of the replicability
#What does replicate pretty well? (CI width?)


#Did the effect replicate?


#Part 2

#Let's see if flexible stopping rules actually affect the probability
#of rejecting the null hypothesis

#Run the first four lines first, this is your "initial data"
#Note that we are sampling both male and female data from a standard
#normal distribution so the population means are equal (i.e., Ho is true)
dep<-rnorm(6)
sex<-rep(c("m","f"),c(3,3))
dat<-data.frame(dep,sex)
t.test(dep ~ sex)

#Add one case at a time, and check for significance each time

#Highlight the following three lines, and repeatedly click on "Run" or
#press ctrl-enter 
# Stop if the p-values goes below .10 (our alpha level) or you get tired

dat[nrow(dat)+1,1]<-rnorm(1)
dat[nrow(dat),2]<-sample(c("m","f"),1)
t.test(dep ~ sex, data=dat)

#Did you find that the effect ever became statistically significant?
#Now imagine that there actually is an effect, even if it's small