Stopping rules are predefined criteria that dictate when a statistical analysis or experiment should be terminated to avoid misleading results or unnecessary resource expenditure. They are crucial in ensuring the integrity and validity of findings, particularly in clinical trials and sequential analysis, by preventing data-driven decisions that could inflate type I error rates.