Statistical tests are used to check if a hypothesis is true or not by seeing if results are likely due to chance. A null hypothesis is set up saying there's no difference between control and experimental samples. Data is collected and analyzed, and the null hypothesis is either accepted or rejected. A significance level is chosen before the experiment to show how certain the results are. Errors can happen where the null hypothesis is rejected when it's true (Type I error) or accepted when it's false (Type II error). The statistical test chosen should match the data collected. It's important to pick the right test before the experiment and tailor the procedures to fit the test for accurate analysis.