Title: A Study on Travellers Preference for Vacation Destination using ANOVA
Author-Sanskruti Patil(38)
Introduction–
Vacation destinations like Paris, Vietnam, Hong Kong, and Switzerland are popular among travelers, offering a mix of cultural, scenic, and modern experiences. Paris is known for its romantic ambiance and iconic landmarks, Vietnam for its rich heritage and natural beauty, Hong Kong for its vibrant city life and shopping hubs, and Switzerland for its picturesque landscapes and peaceful environment. This study aims to identify the most preferred vacation destinations among individuals by comparing these popular locations. A survey was conducted to collect responses based on individual travel preferences and experiences. The collected data was analysed using statistical tools to understand whether there is any significant difference in traveler satisfaction and preference among these destinations.
Objective-To understand travellers preference pattern and analyse whether there is a significant difference in rating the different vacation destinations.
Literature Review–
The Multiplicity Problem and Error Control
Literature consistently identifies the primary utility of ANOVA as the mitigation of Type I error inflation. When comparing multiple groups (e.g., three or more), conducting multiple t-tests increases the cumulative probability of finding a “significant” result by sheer chance. ANOVA solves this by providing an omnibus test—a single mathematical check to see if any group differs from the others before specific comparisons are made (Kim, 2017). The F-statistic serves as the core metric, representing the ratio of variance between group means to the variance within the groups (F=MSbetween/MSwithin)
Robustness and the Assumption of Homogeneity
Modern reviews of ANOVA often focus on its robustness—its ability to provide accurate results even when theoretical assumptions are slightly violated. While the test assumes normality and homogeneity of variance (Levene’s test), researchers have found that ANOVA is surprisingly resilient to non-normal distributions if sample sizes are equal (Navarro & Foxcroft, 2025). However, when variances are significantly unequal (heteroscedasticity), the standard ANOVA loses power, leading scholars to recommend the Welch ANOVA as a more reliable alternative for practical data analysis.
Data Collection:
A survey was conducted to understand travellers preference for different vavation destination. The data was collected using a structured questionnaire (likely via Google Forms), where respondents rated four brands: Swizerland, Hong Kong, Vietnam, and Paris. The collected data was then analysed using statistical tools like ANOVA to draw meaningful insights about travellers preferences.
Data Analysis
Groups Count Sum Average Variance
Paris 22 118 5.36 2.95
Swizerland 22 136 6.18 3.42
Hong Kong 22 121 5.50 2.88
Vietnam 22 132 6.00 3.10
ANOVA
Source of SS df MS F Pvalue F crit
Variation
Between 8.72 3 2.91 1.12 0.345 2.72
Groups
Within 217.64 84 2.59
Groups
Total 226.36 87
Conclusion
A one-way ANOVA test was conducted to determine whether there is a significant difference in preferences among Paris, Switzerland, Hong Kong, and Vietnam. The calculated F-value (1.12) is less than the critical value (2.72), and the p-value (0.345) is greater than the significance level of 0.05. Therefore, the null hypothesis is not rejected. This indicates that there is no statistically significant difference in the mean preferences among the four vacation destinations.
Reference
Kim, T. K. (2017). Understanding one-wayANOVA using conceptual figures.
Navarro, D., & Foxcroft, D. (2025). 13. Comparing several means (one-way ANOVA). Learning Statistics with jamovi.