When running hypothesis tests, it's vital to understand the chance for error. Specifically, we have to grapple with two key types: Type 1 and Type 2. A Type 1 fault, also called a "false positive," occurs when you wrongly reject a true null hypothesis – essentially, suggesting there's an impact when there doesn't really one. Conversely, a Type 2