In the world around us, nothing happens in isolation. One event affects another. The activity of one living thing changes the lives of other organisms. As the natural world is very complicated, it can be difficult to see clearly how and why things are happening. One of the most important factors in designing an experiment is to try to isolate the particular event or substance being studied, so that the results of the experiment are not influenced by other things. For example, to see if a plant needs sunlight to live, you can put it in the dark and watch what happens. But it is important to make sure that the plant still has the same soil, amount of water and temperature as before, so that you can be sure that any changes in the plant are a result of the lack of sunlight.
Many experiments use something called a control. For example, to test a new drug, a hundred people may be given it and their health monitored very carefully. A hundred similar people may be given no drug or a harmless substance and their health monitored just as accurately. They are the control. It is the difference in results between the two groups of people that is important. The control group is designed to show what would have happened to the first group if it had received no drugs. Only then can scientists tell if the drug has had an effect.
An experiment is a type of research method in which you manipulate one or more independent variables and measure their effect on one or more dependent variables. Experimental design means creating a set of procedures to test a hypothesis.
A good experimental design requires a strong understanding of the system you are studying. By first considering the variables and how they are related, you can make predictions that are specific and testable.
How widely and finely you vary your independent variable will determine the level of detail and the external validity of your results. Your decisions about randomization, experimental controls, and independent vs repeated-measures designs will determine the internal validity of your experiment.