When you write a thesis, you almost always want to get the most out of your work:
you want your thesis to be given the highest score;
or you want your thesis to be published and, thus, start a career in research.
In this regard, after having written five theses in my life, the most important lesson that my tutors in
medical statistics provided me with is:
the most appropriate method by which you obtained the results is much more important than the results by their self.
For the quality of your work, it does not matter whether the drug you tested works or doesn’t; it is important that the result is produced through an appropriate study design, a correctly conducted experiment and an adequate statistical analysis of the data.
The design, execution and method must be chosen and justified with an unassailable rational.
This is something that shifts your work from the “ordinary” to “excellent”, as well as, of course, the impact of your research.
But most students and researchers (and even professors), when they are asked why a particular statistical technique has been used, the answer is:
“Because it was used in other studies”;
“Because my software uses this analysis for my type of experiment”;
“Because it is a convention for this type of data”.
These are coded answers to avoid saying “I have no idea”.
Now the question you need to answer yourself, when you analyse your data, is:
“Do I want mediocrity or excellence?”
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