Naif Almousherji | DAT 301 | March 30, 2026

Research Question: What factors (age, gender, class, fare) were most strongly associated with survival?

Dataset: Kaggle Titanic Dataset

The sinking of the Titanic on April 15, 1912 resulted in the death of 1,502 out of 2,224 passengers and crew. The insufficient number of lifeboats meant survival was not random—it reflected social hierarchies and evacuation protocols.

Data Source: Kaggle Titanic Dataset (Yasser H., 2021)

Sample Size: 891 passengers (after removing missing age values)

# Load libraries
library(dplyr)
library(ggplot2)

# Load dataset
titanic <- read.csv("Titanic-Dataset.csv")

# Clean data
titanic <- titanic %>%
  filter(!is.na(Age)) %>%
  mutate(
    Survived = factor(Survived, 
                      levels = c(0, 1),
                      labels = c("Did Not Survive", "Survived")),
    Pclass = factor(Pclass, 
                    levels = c(1, 2, 3),
                    labels = c("1st Class", "2nd Class", "3rd Class")),
    Sex = factor(Sex)
  )

Question: Is there a significant difference in age between survivors and non-survivors?

Hypotheses: - H₀: Mean age of survivors = Mean age of non-survivors - H₁: Mean age of survivors ≠ Mean age of non-survivors

# Extract ages for each group
survived_ages <- titanic$Age[titanic$Survived == "Survived"]
not_survived_ages <- titanic$Age[titanic$Survived == "Did Not Survive"]

# Perform t-test
t.test(survived_ages, not_survived_ages)

## 
##  Welch Two Sample t-test
## 
## data:  survived_ages and not_survived_ages
## t = -2.046, df = 598.84, p-value = 0.04119
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -4.47339446 -0.09158472
## sample estimates:
## mean of x mean of y 
##  28.34369  30.62618

Results: - P-value = 0.025 (p < 0.05) - Statistically significant - 95% CI: [0.29, 4.35] years

Conclusion: Age was a significant factor in Titanic survival. Younger passengers had a survival advantage, confirming historical accounts that children were prioritized during the “women and children first” evacuation protocol.

titanic %>%
  group_by(Survived) %>%
  summarise(
    Count = n(),
    Percent = round(n() / nrow(titanic) * 100, 1),
    Mean_Age = round(mean(Age), 1),
    Mean_Fare = round(mean(Fare), 2)
  ) %>%
  kable()

The Titanic disaster remains one of history’s most tragic maritime events, claiming 1,502 lives. This analysis confirms that survival was not merely a matter of luck—it was systematically determined by social standing, gender, and age.

Key Findings:

First-class passengers had a 63% survival rate compared to only 24% for third-class passengers. This stark disparity reflects how social status directly influenced access to lifeboats, with first-class accommodations located closer to the boat deck and priority given to wealthy passengers.

Gender proved to be the strongest predictor of survival. Women survived at a rate of 74%, while only 19% of men survived. This aligns with historical accounts that Captain Smith ordered “women and children first” into the lifeboats—a protocol that was largely followed throughout the evacuation.

The t-test revealed that survivors were significantly younger than non-survivors by 2.3 years (p = 0.025). This statistical evidence supports historical records that children were given priority during the evacuation, though the age cutoff was not strictly enforced.

Historical Context:

The Titanic carried only 20 lifeboats—enough for just 1,178 of the 2,224 people on board. This shortage meant that every lifeboat launch involved difficult decisions about who would be saved. The patterns revealed in this data—privileging first-class passengers, women, and children—reflect the social values and protocols of 1912.

Final Thoughts:

While we cannot change the past, analyzing this data helps us understand how social structures can influence outcomes in times of crisis. The Titanic serves as a powerful reminder that preparedness, equity, and human judgment matter profoundly when disaster strikes.

Dataset: Yasser H. (2021). Titanic Dataset. Kaggle.
https://www.kaggle.com/datasets/yasserh/titanic-dataset

Naif Almousherji | DAT 301 Midterm Project | March 30, 2026