Automatic Statistical Analysis and Reporting in Survey Research: A Comparative Evaluation of the AI-Based System Pollsensei and Traditional Methods

Abstract

In survey research, the rapid growth in data availability has not been matched by parallel advancements in tools that make analysis and interpretation more accessible. Organizations across sectors: including education, public health, NGOs, and small businesses, often face significant barriers in transforming raw survey data into actionable insights due to limited statistical expertise, time constraints, and the complexity of traditional analysis tools. This paper introduces PollSensei, an AI-powered system designed to automate end-to-end from survey data collection to statistical analysis and natural language reporting for survey-based research. The platform integrates variable classification, test selection, inferential analysis, and narrative generation into a seamless, user-friendly pipeline. At its core, PollSensei combines a statistical reasoning engine with a customizable natural language generator to produce reports that are both technically accurate and communicatively effective, tailored to audiences ranging from academic researchers to NGO practitioners and policy stakeholders. This paper presents the system architecture in detail, demonstrating how PollSensei handles descriptive statistics, inferential tests such as ANOVA, T-tests, and Chi-square, as well as sentiment scoring for qualitative responses. Furthermore, we outline the methodology for evaluating PollSensei’s performance against traditional manual workflows, using real-world-inspired survey datasets. By automating both statistical reasoning and interpretive reporting, PollSensei aims to reduce reliance on technical specialists and accelerate the transformation of data into decision-ready insights.