Quality of right evidence

Posted by on Jan 2, 2011 in Blog, Papers | No Comments

Advancement in science is an iterative process; a theory is proposed upon certain observations, then experiments are conducted to validate its authenticity. Most of the time, this step is followed by going back to the drawing board and redoing the experiments again. After all these years of practise, learning new and complex field of science is still harder (Stewart, 2010) than it was in the past. One might wonder what kind of evidence we must use to progress in a field of science such as ecology, as the evidence can come from several different sources. Samuel Scheiner in his book, The Nature of Scientific Evidence, states that “all kinds of evidence must be used in building and testing these theories”. (Scheiner, 2004, p. 10) Scheiner believes that it is not the observation that is important, but is it the right one for the problem at hand. If one is looking at evidence with only a part of the overall scope of the project, then the chances of knowing the quality of the observation and evidence is also significantly reduced with it.

True to the above statement, sometimes, while working on a project, I tend to easily overlook important guidelines depending on how I approach the project. A particular example that comes to mind is one regarding a programming assignment I was working on. I got stuck midway through the problem and tried various solutions to fix the problem I was having but to no avail. However, when I showed the problem to a friend, he immediately solved the problem. When he explained to me the methodology he worked through to reach his solution, I realized that I was on the right track the whole time and my mistake lied in a procedure I had earlier dismissed without proper investigation. I was stuck because I did not realize that I had good evidence in hand the whole time. Terry Stewart, a researcher in the field of Artificial Intelligence, describes that for complex problems, a multi-tier level of solutions is necessary and increases the chances of having good evidence. The problem arises when more than one person is involved in a project and thus, the nature of evidence gathering gets complex and the chances of them being documented badly is high. When evidence is not documented properly, they can easily be ignored or overlooked and even worse, lost completely thereby wasting the efforts of the entire investigation.

Goretty Dias, a leading researcher in the field of GHG (greenhouse gas) emissions and LCA (Life cycle assessment), while talking to our INTEG 220: Nature of Knowledge class, brought up the importance of collaboration for the advancement in research. While narrating her personal experiences she talked about a time when she was initially excited at the prospects of doing inter disciplinary collaboration with some other party of researchers. However, she later found out that there was a big problem with this new system (or so called collaboration); the buy-in amongst the researchers. Upon receiving the funding for the collaboration, the researchers were found to simply carry on with their normal day to day work and research in their respective locations and only came together once a year to present their findings (Dias, 2010). Dias also described about how, a lot of the research work that the professor is supposed to do, trickles down to the PhD or to even a master’s student. The problem with this was that the students, though very knowledgeable, simply did not have a bigger scope or picture of the problem they were dealing with in the project and were prone to making mistakes and overlooking crucial details due to lack of experience. As mentioned before, by not knowing the scope of the whole project, the master’s or PhD student could very easily overlook the real evidence or discard it as a whole.

Though collaboration in science is difficult, a lot of wonderful developments have occurred through the collaboration amongst different fields of sciences. One of the big problems with the collaboration process is that information can easily be lost through ineffective communication means. Sometimes, having a correct solution is not as important as the ability to explain your solutions to your peers in a group collaborative project. By having a collaborative and multi-tier level approach to solving solutions of complex problems in science, it is easier to acquire the right evidence during observations and experiments to tackle the problem at hand. But when a person engaged in the collaborative research is not well versed in the extent or scope of the project, such as a seasonal PhD or master’s student, it is very easy to lose the right evidence.

Dias, G. (2010). INTEG 220 Guest Lecture.
Scheiner, S. (2004). Nature of scientific evidence.
Stewart, T. (2010). Ways of Knowing: Characterizing the Behaviour of a Robot.

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