Learning T-Tests

Load Packages

Import and Export SPSS, STATA, and SAS files

if(!require(haven)){
  install.packages("haven", dependencies = TRUE)
  library(haven)}

Loading required package: haven

A collection of packages that makes it easy to tidy, clean, and work with data.

if(!require(tidyverse)){
  install.packages("tidyverse", dependencies = TRUE)
  library(tidyverse)}

Loading required package: tidyverse

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.2     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.2     ✔ tibble    3.2.1
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
✔ purrr     1.0.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Import Data

dataset <- read_sav("https://osf.io/kd4ej/download")

t-test

t.test(formula = Potter5 ~ CoinFlip,
       data = dataset,
       var.equal = FALSE)


    Welch Two Sample t-test

data:  Potter5 by CoinFlip
t = -3.9177, df = 52.353, p-value = 0.0002606
alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
95 percent confidence interval:
 -0.6030108 -0.1945582
sample estimates:
mean in group 1 mean in group 2 
       1.246377        1.645161