A #glfintech R turorial on statistics discussing hypothesis tests - parametric tests (z-test, t-test and ANOVA) and non-parametric tests (Mann-Whitney Test, Wilcoxon Rank-sum test, Wilcoxon Signed-rank Test, Kruskal Wallis Test).

R Tutorials | t-tests in R | #rstatistics

#Conducting t-tests in R.
t.test(death_rt)
t.test(lifeexpf, lifeexpm)
t.test(lifeexpf, lifeexpm, var.equal=T)
t.test(lifeexpf, lifeexpm, paired=T)

#A function for creating random examples of t-tests
ttest.for.examination <- function(x,y,z,k)
{
  subjects <- x
  mean1 <- y
  mean2 <- z
  standarddev <- k
  print( c("Number of measurements:   ", x))
  print( c("Mean of group 1: ", y))
  print( c( "Mean of group 2: ",z))
  print( c("Standard deviation:  ", k))
  group1 <- round(rnorm(x, y, k))
  group2 <- round(rnorm(x, z, k))
  framedata <- cbind(group1, group2)
  print(framedata)
  print( list (t.test(group1, group2, var.equal = T), t.test(group1, group2, var.equal = T, paired = T)))
}
#Now you can run the new function.
ttest.for.examination(13,90,105,10)
ttest.for.examination(13,92,100,10)
#Try running the same arguments twice
ttest.for.examination(13,92,100,10)