Matching methods are statistical techniques used to create comparable groups in research studies. They aim to reduce bias by ensuring that participants in different groups share similar characteristics, such as age, gender, or health status. This helps researchers draw more accurate conclusions about the effects of an intervention or treatment. Common matching methods include propensity score matching, where participants are paired based on their likelihood of receiving a treatment, and exact matching, which pairs individuals with identical characteristics. These methods enhance the validity of observational studies by mimicking the conditions of randomized controlled trials.