GPT-4V's capabilities in analyzing visual data like bar charts, scatter plots, and tables were put to the test in a series of experiments to assess its proficiency. The study focused on comparing the effectiveness of various questioning techniques—general inquiries, specific questions, and chain of thought prompting—to enhance the accuracy and reliability of the model's responses. The findings indicated that specific questions generally yielded better results, although the model sometimes exhibited hallucinations, particularly in interpreting details like model parameters and performance metrics across different tasks. The experiments revealed that while GPT-4V can provide insightful analyses, its outputs can be inconsistent and prone to errors, suggesting that repeated experiments might yield varying results. The study emphasized the importance of precise questioning and systematic reasoning in leveraging GPT-4V's full potential, while also highlighting the need for caution when drawing conclusions from its analyses due to its tendency for elevated levels of hallucination in certain contexts.