Many statistical applications focus on understanding a binary characteristic of an object or a person, where this characteristic is observed through a test. When sampled items can be pooled to form a new single unit, such as with blood specimens to be tested for an infectious disease, a process known as group testing (also known as pooled testing, batch testing, and specimen pooling) can be used to understand this binary characteristic. Applied in appropriate settings, group testing can greatly reduce associated testing costs and increase testing efficiency. This is why group testing has played an important role with increasing testing capacity during the COVID-19 pandemic. Group testing has two separate goals – identification and estimation – where one or both of them may be goals for a particular application. In our presentation, we focus on the identification goal to determine a binary outcome, such as a positive/negative outcome for a disease.