When you’re comparing groups of data, it’s helpful to know whether the differences you see are meaningful or just random chance. That’s where statistical tools like Z-tests and T-tests come in. These tests are examples of inferential statistics, which help you make conclusions about a larger population based on a smaller sample.
Both the Z-test and the T-test are used in hypothesis testing, where you’re trying to either prove or disprove an assumption. For example, you might have a hypothesis that a new drug is more effective than an existing one. By running a Z-test or a T-test on data from a clinical trial, you can find out if your hypothesis is likely to be true.
But how do you know which test to use? The choice between a Z-test vs a T-test depends on several factors, including the size of your sample, whether you know the population’s standard deviation, and the type of data you’re working with.
Let’s take a closer look at these two statistical tests, exploring their applications and the key differences between them so you can choose the right tool for your analysis.