Data analysis is something you already do after each experiment when you write your results and conclusions. Seeing a result once on one biological sample is not sufficient to claim that you have made a scientific discovery. Maybe you got lucky or are observing a false positive. Perhaps you are actually observing a false negative or made a mistake; if you repeated the experiment you might see positive results instead. In any case, one experiment does not provide enough evidence to support or refute a hypothesis. It is very important to have good positive and negative controls and consistent standards for comparison.
In the following learning activities, we focus on comparing data from several experiments to find averages and to determine statistical significance in results.