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library(readxl)
CerinaData <- read_excel("C:/Users/leocint/Desktop/CerinaData.xlsx")
View(CerinaData)

library(tidyverse)

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

## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.0.6     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

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

library(dplyr)
view(CerinaData)

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

d<- t.test(New$`Post-test Score`,New$`Pre-test Score`,paired = TRUE)
d

## 
##  Paired t-test
## 
## data:  New$`Post-test Score` and New$`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

Interpretation of the Results

The p-value of the the test is 0.1161, which is greater than the significance level alpha = 0.05. We can then accept the null hypothesis and conclude that there is no significant difference in the Post-test Score and Pre-test Score.