UFO Sightings & Political Alignment
Statistical Analysis of U.S. UFO Reports
Do political differences influence reported UFO sightings?
Using over 100,000 public UFO reports, I investigated whether political alignment is associated with differences in reported sightings, narrative characteristics, and reporting behavior across U.S. states.
View Source Code View Research Report View Research Poster ← Back to Projects
Role
Statistical Researcher
Domain
Public Data Analytics
Stack
R · Tidyverse · NLP
Methods
Hypothesis Testing · Regression · Text Mining
Project Snapshot
Question
Do Democratic and Republican states differ in UFO reporting behavior?
Approach
Combined statistical hypothesis testing, regression modeling, and text analysis on national UFO sighting records.
Findings
Political alignment was associated with reporting frequency, while narrative characteristics remained remarkably similar.
Overview
Thousands of UFO sightings are reported across the United States every year. While these reports are often studied individually, much less attention has been given to broader geographic and political patterns.
This project investigates whether political alignment is associated with differences in UFO reporting frequency, reported object characteristics, and witness narratives using publicly available data from the National UFO Reporting Center.
Research Questions
Reporting Frequency
Do Democratic and Republican states report different numbers of UFO sightings?
Statistical Significance
Are observed differences statistically significant or likely due to chance?
Narrative Analysis
Do witnesses describe sightings differently depending on political alignment?
Predictive Modeling
Can political alignment explain variation in reported sightings after accounting for statistical uncertainty?
Analysis Pipeline
Data Collection
Imported national UFO sighting records.
Data Cleaning
Prepared state-level observations and standardized variables.
Statistical Testing
Performed Mann–Whitney and Chi-square hypothesis tests.
Regression
Fit Poisson regression models to estimate reporting differences.
Text Analysis
Compared narrative descriptions across political groups.
Interpretation
Summarized statistical evidence and practical implications.
End-to-end statistical workflow combining inference, modeling, and NLP.
Technical Stack
- R
- Tidyverse
- Hypothesis Testing
- Poisson Regression
- Mann–Whitney Test
- Chi-Square Test
- Natural Language Processing
- Data Visualization
- Statistical Inference
What I Built
Data Pipeline
Prepared and cleaned a nationwide UFO reporting dataset for statistical analysis.
Statistical Analysis
Applied multiple hypothesis tests to evaluate differences between political groups.
Predictive Modeling
Built Poisson regression models to quantify the relationship between political alignment and reporting frequency.
Narrative Exploration
Analyzed witness descriptions using natural language processing techniques to compare reporting patterns.
Key Findings
UFO reports analyzed
Independent statistical methods agreed on reporting differences
Narrative descriptions were broadly similar across political groups
What I Learned
This project strengthened my experience applying statistical inference to large observational datasets. Rather than relying on a single statistical test, I combined nonparametric testing, generalized linear models, and exploratory text analysis to evaluate the same research question from multiple perspectives.
It also reinforced the importance of distinguishing statistical significance from practical significance. Even when differences exist in reporting frequency, interpretation requires careful consideration of potential confounding factors such as population size, reporting culture, and geographic variation.
Research Deliverables
The complete statistical report and research poster are available below.