Inferential statistics are used to test hypotheses about the relationship between the Dependent Variable (DV) and the Independent Variable (IV). Your choice of test depends on experimental design, level of measurement, and assumptions about the DV's normality of distribution. All tests discussed here are for comparing two groups or conditions.

• Parametric tests (e.g., related t-test and unrelated t-test) assume a normal distribution and use parameters like the mean and standard deviation.
• Non-parametric tests don't require normal distribution because they don't use mean or standard deviation in their calculations.

🔔 Real-life example: Let's consider an experiment where we want to find out if caffeine affects students' performance. Here, the DV is the students' performance, and the IV is the presence or absence of caffeine. If our data shows a normal distribution, we might use a parametric test like the t-test.