t-test

library(readxl)

## Warning: package 'readxl' was built under R version 4.0.5

CerinaData <- read_excel("C:/akong project/seatwork/CerinaData.xlsx")
View(CerinaData)

library(tidyverse)

## Warning: package 'tidyverse' was built under R version 4.0.5

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.1.5     v dplyr   1.0.5
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1

## Warning: package 'tibble' was built under R version 4.0.5

## Warning: package 'tidyr' was built under R version 4.0.5

## Warning: package 'readr' was built under R version 4.0.5

## Warning: package 'purrr' was built under R version 4.0.4

## Warning: package 'dplyr' was built under R version 4.0.4

## Warning: package 'forcats' was built under R version 4.0.5

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

view(CerinaData)

x<-na.omit(CerinaData)%>%
view() 

t.test(x$`Post-test Score`,x$`Pre-test Score`,alternative = "two.sided",var.equal = T,paired = T)

## 
##  Paired t-test
## 
## data:  x$`Post-test Score` and x$`Pre-test Score`
## t = 1.6361, df = 22, p-value = 0.1161
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.2559605  2.1690040
## sample estimates:
## mean of the differences 
##               0.9565217

Conclusion: Accept null hypothesis: There is no significant increase from pre test scores to post test scores.