Do the Numbers Match the Plot? Statistical-Visual Verification for Automated Exploratory Data Analysis

Abstract

Automated exploratory data analysis (EDA) systems can execute statistics correctly while producing misleading interpretations. For example, correlations may be driven by influential observations, and mean differences may arise from skewness, overlap, or sparse subgroups rather than broad distributional shifts. We present EDAgent, a sequential framework that performs targeted visual verification before report synthesis. For each finding, the system records the statistical result, generates a visualization request identifying relevant distributional features, creates the corresponding chart, and uses the observed pattern to retain, qualify, or reconsider the interpretation. We call this process statistical-visual verification. EDAgent includes VisPlan, an 8B-parameter model that converts visualization rationales into detailed chart plans. We evaluate 87 end-to-end reports on EDA-Bench using a six-dimension rubric and GPT-5.1 multimodal evaluation. Maximum-score proportions reached 87.4% for factuality and visual consistency, 93.1% for statistical-visual cross-validation, and 96.6% for analytical narrative. On 166 open-ended VisEval tasks, VisPlan improved the mean overall score from 4.55 (Qwen3-8B) to 4.62, with the largest gain in aesthetics. These descriptive results indicate that visual verification can make automated EDA reports more transparent about how statistical findings relate to underlying data structure.