cps <- read.table("http://s3.amazonaws.com/assets.datacamp.com/production/course_1276/datasets/cps_ch3.csv", header = T, sep = ";")
tstat <- (mean(cps$ahe12)-23.5)/(sd(cps$ahe12)/sqrt(length(cps$ahe12)))
pval <- 1-pnorm(tstat)
# conduct the hypothesis test from the previous exercises with t.test()
# extract t statistic and p-value from the list created by t.test()
# verify that using the normal approximation is valid here as well
# conduct the hypothesis test from the previous exercises with t.test()
t.test(cps$ahe12, alternative = "greater", mu = 23.5)
# extract t statistic and p-value from the list created by t.test()
tstat <- t.test(cps$ahe12, alternative = "greater", mu = 23.5)$statistic
pvalue <- t.test(cps$ahe12, alternative = "greater", mu = 23.5)$p.value
# verify that using the normal approximation is valid here as well
pvalue - pval
test_function_result("t.test")
test_object("tstat")
test_object("pvalue")
test_or({
test_student_typed("pvalue - pval")
},{
test_student_typed("pval - pvalue")
},{
test_student_typed("pval == pvalue")
},{
test_student_typed("pvalue == pval")
})
success_msg("Correct! The difference between both p-values is very small so using the normal approximation leads to the same conclusion here.")