Research Scenario 2

INDEPENDENT T-TEST & MANN-WHITNEY U TEST

What are the null and alternate hypotheses for YOUR research scenario?
H0:There is no difference between two groups (Human and AI)
H1: There is difference between two groups (Human and AI)

LOAD THE PACKAGE

library(readxl)

IMPORT EXCEL FILE INTO R STUDIO

dataset <- read_excel("C:/Users/burug/Downloads/A6R2.xlsx")

LOAD THE PACKAGE

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

CALCULATE THE DESCRIPTIVE STATISTICS

dataset %>%
  group_by(ServiceType) %>%
  summarise(
    Mean = mean(SatisfactionScore, na.rm = TRUE),
    Median = median(SatisfactionScore, na.rm = TRUE),
    SD = sd(SatisfactionScore, na.rm = TRUE),
    N = n()
  )

## # A tibble: 2 × 5
##   ServiceType  Mean Median    SD     N
##   <chr>       <dbl>  <dbl> <dbl> <int>
## 1 AI           3.6       3  1.60   100
## 2 Human        7.42      8  1.44   100

CREATE THE HISTOGRAMS

hist(dataset$SatisfactionScore[dataset$ServiceType == "Human"],
     main = "Histogram of Group 1 Scores",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightblue",
     border = "black",
     breaks = 20)

hist(dataset$SatisfactionScore[dataset$ServiceType == "AI"],
     main = "Histogram of Group 2 Scores",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightgreen",
     border = "black",
     breaks = 20)

Q1) Check the SKEWNESS of the VARIABLE 1 histogram. In your opinion, does the histogram look symmetrical, positively skewed, or negatively skewed?
A1: Negatively skewed.
Q2) Check the KURTOSIS of the VARIABLE 1 histogram. In your opinion, does the histogram look too flat, too tall, or does it have a proper bell curve?
A2: Too tall
Q3) Check the SKEWNESS of the VARIABLE 2 histogram. In your opinion, does the histogram look symmetrical, positively skewed, or negatively skewed?
A3: Positively Skewed
Q4) Check the KUROTSIS of the VARIABLE 2 histogram. In your opinion, does the histogram look too flat, too tall, or does it have a proper bell curve?
A4: Too tall

CONDUCT THE SHAPIRO-WILK TEST

shapiro.test(dataset$SatisfactionScore[dataset$ServiceType == "Human"])

## 
##  Shapiro-Wilk normality test
## 
## data:  dataset$SatisfactionScore[dataset$ServiceType == "Human"]
## W = 0.93741, p-value = 0.0001344

shapiro.test(dataset$SatisfactionScore[dataset$ServiceType == "AI"])

## 
##  Shapiro-Wilk normality test
## 
## data:  dataset$SatisfactionScore[dataset$ServiceType == "AI"]
## W = 0.91143, p-value = 5.083e-06

Q1: Was the data normally distributed for Variable 1?
A1 : The p = 0.00013, So the data is not normal
Was the data normally distributed for Variable 2?
A2 : The p = 5.083e-06 ,so the data is not normal

LOAD THE PACKAGE

library(ggplot2)
library(ggpubr)

CREATE THE BOXPLOT

ggboxplot(dataset, x = "ServiceType", y = "SatisfactionScore",
          color = "ServiceType",
          palette = "jco",
          add = "jitter")

Q1) Were there any dots outside of the boxplot? Are these dots close to the whiskers of the boxplot (check if there are any dots past the lines on the boxes) or are they very far away?
A1 : Yes, there are few dots far away from the whiskers

MANN-WHITNEY U TEST

wilcox.test(SatisfactionScore ~ ServiceType, data = dataset, exact = FALSE)

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  SatisfactionScore by ServiceType
## W = 497, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0

LOAD THE PACKAGE

library(effectsize)

CALCULATE EFFECT SIZE (R VALUE)

rank_biserial(SatisfactionScore ~ ServiceType, data = dataset, exact = FALSE)

## r (rank biserial) |         95% CI
## ----------------------------------
## -0.90             | [-0.93, -0.87]

Q1) What is the size of the effect?

A1: Cohen’s D of 0.90 indicates the difference between the group averages was very large.

Q2) Which group had the higher average rank?

A2 : Human Satisfaction Score is higher than the AI Satisfaction Score .

REPORT
A Mann-Whitney U test was conducted to compare Satisfaction scores between human Service (n=100) and AI Service (n=100).Human Service had significantly higher median scores (Mdn = 8.00) than the AI service (Mdn = 3.00), p < 2.2e-16 The effect size was moderate (r = 0.90), indicating a meaningful difference between Satisfaction scores. Overall, As per comparing human service satisfaction score is higher than the AI satisfaction Score.