Among scientist there is a famous saying “standing on the shoulders of giants”. The saying emphasizes importance of learning from the past researchers, the giants. By understanding past thinking, research and experiment, metaphorically, we are able to see further and much broader view.
Doing experiment without considering the past working is the same as fooling yourself. The problems you are working on might have been solved by other scientist, or the questions you are asking have been answered by others. Thus, the work you are doing is a waste of time.
In academic institution, learning from past research is the first step of scientific methods. Before you make an experiment or make hyphothesis, you should and must read past documentation related to research you are working on. The source of information has to be scientifically valid. You cannot pick any random information from internet or newspaper. The best source of information are coming from international scientific journal because it is scientifically valid and peer-reviewed by other scientist who is working on the same subject.
Learning from the past is not only confined in academic community. In practical world, where scientific methods is flexibly applied, learning from the previous experiment is equally important. Before you put your hand to solve a problems, you have to gather information about the problem itself.
Although writing habit is not as formal as scientific community, we still need to seek information. “Giants” in practical world often don’t follow rigid scientific methods. We know the term “best practice” on how things work in an organization. “Best practice” comes from years of experience in one subject. This is one that we couldn’t find in scientific journals for sure.
It is just silly when you see someone don’t understand the basic concept of problem solving , especially one that hold important position. How come you jump into experiment without sufficient data and planning. Furthermore, the experiment would consume resources. Without good solid understanding about the problem, we can surely predict that the experiment would be a failure.
My oh my…