Statistical writing is a key part of the undergraduate statistics curriculum, both because it helps students reason about complex ideas and because it teaches them an important professional skill. Through writing, students learn to construct arguments about data, present evidence, and communicate results. Large language models (LLMs) thus raise important questions about the role of writing in statistics education: can they serve as useful tools for students still mastering writing, or do they impede development of important skills? We describe recent research comparing the style and structure of student-, expert-, and LLM-written statistical reports, revealing unexpected biases in the writing style of LLMs, and discuss the implications for students still learning to write like experts.