One-sample T-test

Paired sample T-test

Compare differences between two dependent means. Commonly applied for case-control studies or repeated measures. Assumptions of paired sample t-test

1. Dependent variable must be numeric or continuous
2. Dependent variable should be normally distributed
3. Variace across two measures should not be statistically different.

Install required packages

install.packages(“dplyr”)

install.packages(“ggpubr”)

install.packages(“stats”)

#Remember if you have already installed these packages, it is not required to install them again and again.

Load required packages

library(dplyr)

library(ggpubr)

library(stats)

Load data

Copy your data in excel and run this command or there are several other option to import your data.

data=read.table(“clipboard”, header=TRUE)

#I will prepare one data for the test, where a training was conducted to improve knowledge of participants of ICT. Their scores were measured before and after the training.

before=c(12.2, 14.6, 13.4, 11.2, 12.7, 10.4, 15.8, 13.9, 9.5, 14.2)

after=c(13.5, 15.2, 13.6, 12.8, 13.7, 11.3, 16.5, 13.4, 8.7, 14.6)

#Create data frame ICT=data.frame(time=rep(c(“before”,”after”), each=10), score=c(before, after))

print(data)

Check normality

shapiro.test(ICT$score) # Data is normally distributed

Check homogeneity of variance

bartlett.test(score~time, data=ICT) # Data has equal variance

Paired sample T-test

t.test(formula = score ~ time, data=ICT, alternative = “greater”, mu = 0, paired = TRUE, var.equal = TRUE, conf.level = 0.95)

Visualize data

ggboxplot(ICT, x = “time”, y = “score”, color = “time”, palette = c(“#00AFBB”, “#E7B800”), order = c(“before”, “after”), ylab = “Score”, xlab = “Time”)

Independent sample T-test