Monday, January 18, 2016

How to become a qualified future Scientist

After viewing the video of deception and reading articles about unreality in science, I feel more pressured of becoming a qualified future scientist than ever.  This is good as it makes me, a graduate student in biomedical science, to think deeply about what's important to carry on along our way of pursuing a scientific career.

As mentioned in Dan Ariely's video, there might be a "fudge factor" which underlines our cheating behaviour.  If people signed the honor code before the test, their cheating behaviour was greatly reduced.  It signifies the importance of awareness.  The awareness that biased research might cause very "bad impact" should be beared in our minds before we design our research, during our data collection and at pre-publication status.  "Bad impact" might be the failure of a drug, the waste of time and cost into the research, or the damage to our reputation in the field. As a student in cancer research, attempting to find a "cure" to pediatric brain cancer, one "bad impact" might be creating the "false hope" to those young patients and their parents or family.

Other than being aware of possible bad impact, it is also important for us biologists to be armed with basic concepts of statistical knowledge. In Jeremy Berg's blog and the article of "Trouble at the lab", they mentioned that scientists tend to produce false positive data and barely publish false negative data, which is more reliable from the perspective of statistics.  This may explain for the most biomedical non- reproducible experiments.  If we knows better about statistics, we would at least not overstate our conclusions considering the possibility of producing false positive result is not that low.  Though no statement is 100 percent true in science as mentioned by Jared Horvath, there must be ways for us to improve our methodology in conducting and communicating science, for example, better interpreting our data.  Nowadays, peer reviews call more attention on statistical explanation, which might attribute to more involving of statistics in biological research.

Now realizing that we are under the age of "publish or perish", we should avoid being pushed to nowhere by the tides of publication stress.  A good start to change might be the awareness of possible bad impact on our society by uncareful research and the integrating of biostatistics in biomedical science.

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