What are the null and alternate hypotheses for YOUR research scenario?

Null hypotheses :- There is no difference in the average customer satisfaction scores between customers served by human agents and customers served by AI chatbot.

Alternate Hypotheses :- There is a difference in the average customer satisfaction scores between customers served by human agents and customers served by AI chatbot.

Result :- A Mann-Whitney U test was conducted to compare customer satisfaction scores between customers served by human agents and those served by an AI chatbot(n = 200). Results showed that customers served by human agents (m = 7.42, Mdn = 8 sd = 1.44) reported significantly higher satisfaction than those served by the AI chatbot (m = 3.6, Mdn = 3 sd = 1.60), W = 497, p < 0.001. The effect size was large (r = 0.90), indicating a substantial and meaningful difference in satisfaction between the two service types. The alternate hypotheses is supported, indicating that there is a difference in the average customer satisfaction scores between customers served by human agents and customers served by AI chatbot.

# install.packages("readxl")
library(readxl)
dataset <- read_excel("~/Downloads/A6R2.xlsx")
#install.packages("dplyr")
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
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
hist(dataset$SatisfactionScore[dataset$ServiceType == "Human"],
main = "Histogram of Group 1 SatisfactionScore",
xlab = "Value",
ylab = "Frequency",
col = "lightblue",
border = "black",
breaks = 20)

hist(dataset$SatisfactionScore[dataset$ServiceType == "AI"],
main = "Histogram of Group 2 SatisfactionScore",
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?

The skewness of the variable 1 is 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?

The kurtosis of the variable 1 is tall

Q3) Check the SKEWNESS of the VARIABLE 2 histogram. In your opinion, does the histogram look symmetrical, positively skewed, or negatively skewed?

The skewness of the variable 2 is 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?

The kurtosis of the variable 2 is tall

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

Was the data normally distributed for Variable 1?

No

Was the data normally distributed for Variable 2?

No

# install.packages("ggplot2")
# install.packages("ggpubr")
library(ggplot2)
library(ggpubr)
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?

If there are many dots (more than one or two) and they are very far away from the whiskers, you should switch to the 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
# install.packages("effectsize")
library(effectsize)
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?

The difference between the two groups is large.

Q2) Which group had the higher average rank?

The average customer satisfaction scores between customers served by human agents is higher than AI chatbot.