Every good experiment starts with a hypothesis, which is just an educated guess about what might happen. You also need a null hypothesis, which assumes that nothing will change, and an alternative hypothesis, which predicts that something will change. For example, if you’re testing a new button on your website, your hypothesis might be, “Adding this button will increase sign-ups.” In surveys, you might test different ad designs to see which one people like more.

To check if your results are real and not just random, statisticians use tools like z-scores (which test if there’s really no difference) and p-values (which show how strong the evidence is for your hypothesis). Another key decision is whether to use a one-sided test (which assumes your change will have a specific effect, like increasing sales) or a two-sided test (which checks if it could have either a positive or negative impact). Luckily, you don’t have to do all this math yourself—there are online calculators and tools that can quickly tell you if your test results are meaningful or just a fluke!