In scientific data gathering, there are three different ways to find the data needed to look at the relationships being studied. These three methods will tell you different things, and have their own strengths and weaknesses. The three methods are the experimental, the quasi experimental, and the correlation method.
In the experimental method, the scientist is trying to isolate causation from a single variable. To see if a particular variable has a consequence the scientist must hold all other variable to be the same and only change the one thing that they are studying. To make sure that the experiment is being done correctly, the choice of who is exposed to the independent variable must be at random (Salkind 2014 p. 8). For example, say that the scientist thinks that college men wearing a baseball cap will be able to run faster. The scientist will then distribute baseball caps to the study group and then measure the running speeds of all the participants. If the study finds that the average speed of the baseball cap wears was in fact faster than the average speed of the non-cap wearers, then the hypothesis is confirmed.
Not all variables are as easily tested as the question of speed of college-age men and baseball cap wearing. Sometimes what is to be measured is not so easy to control. If the experiment designer cannot pick who receives a variable, then there is a measure of control lost. This becomes part of a quasi-experimental method (Salkind 2014 p. 9). An example where a quasi-experimental method would be used is one where a scientist was curious at who was better at chess, all other things being equal, left-handed people or right-handed people. Since nature has already chosen who will be left-handed and who will be right handed, the element of randomness has been taken away from the experimenter. The ultimate results of the quasi-experiment may be less certain than a strictly experimental method because there may be other variables that the left-handers possess other than their dominate hand that may skew the results.
The final method for looking at relationships between two variables is the correlational method. In this method, there is no experiment run, but the scientist looks at two sets of data to see if there is a relationship between them. Does one go up while at the same time the other goes down? Alternatively, do they move in tandem together? If they do either one, then the indicators are said to be correlated. The problem with looking for correlation is that scientist cannot tell if there is a direct causal relationship (Salkind 2014 p. 10). Say a scientist can look at the sales of Happy Meals in America as well as the average weight of American children. If the scientist sees that both variables increased over the same time, then a correlation can be said to exist. The issue is that there is no way to say directly what caused what. Did children gain weight because they were eating too many Happy Meals, or did already-obese children demand more Happy Meals?
The overall result is that the more control a scientist has over the independent variables that they are studying, the more certain they can be with the validity of their results. In the use of data, more control is the desired starting point, but it may not always be possible to attain. That is why the other options exist.