We investigate the potential for Large Language Models (LLMs) to enhance scientific practice within experimentation by identifying key areas, directions, and implications. First, we discuss how these models can improve experimental design, including improving the elicitation wording, coding experiments, and producing documentation. Second, we discuss the implementation of experiments using LLMs, focusing on enhancing causal inference by creating consistent experiences, improving comprehension of instructions, and monitoring participant engagement in real time. Third, we highlight how LLMs can help analyze experimental data, including pre-processing, data cleaning, and other analytical tasks while helping reviewers and replicators investigate studies. Each of these tasks improves the probability of reporting accurate findings.