Monday, January 16, 2017

The Pressure to Publish

In my short time as a biomedical PhD student, there has been an evident ideal surrounding the field that the more papers you publish and the higher the impact factor of the journals you publish to, the better the scientist you are. This idea has been ingrained in the minds of most scientists and while this is often a good measure of how much work you have done during your scientific career, it is not necessarily a strong measure of how good of a scientist you actually are. This is mainly due to the ever-increasing standards of the high level research journals that desire new and exciting data, which results in scientific researchers cutting corners in order to find the easiest way to the most novel data. One major cornerstone of research that has been cut in this process is effective use of statistics in data analysis and experimental design.

In a recent interview by Martin Hagger, psychology professor at Curtin University in Australia, he identified using correct sample size numbers as one casualty of the current age of research; "researchers have been driven to finding significant effects, finding things that are novel, testing them on relatively small samples". While this goes against many ethical standards, the largest issue with using small sample sizes is that it biases the data towards the desired results of the scientist, not the data that represents what is actually happening in the experiment. The data is not wrong, but it is not true either. In this case, the scientist may get the data published but it is not the data of a good scientist; it is the data of a scientist that used his bias to guide his experimental technique.

My belief is that many cases of bad research occur because of the pressure to publish, and more specifically, the pressure to publish positive data. While positive data is most widely agreed to be the data that progresses the overall knowledge in the field, negative data has its place in research as well. Positive data reinforces the decisions we make in research but negative data forces us to change our way of thought and explore ideas that we hadn't previously thought of before. Unfortunately there is not much reward for obtaining negative data, other than the potential for finding new pathways to positive data. However, in order to reinforce the value of a good scientist and going through the correct processes to find the most novel and exciting data, scientists must know there can be value in obtaining negative data.

Link to interview:

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