The relevance of ontological commitments

In this introductory note, I describe my particular view of the notion of ontological commitments as honest and pragmatic working hypotheses that assume the existence (out there) of certain entities represented by the symbols in our theory. I argue that this is not naive, in the sense that it does not entail the belief that the hypotheses could ever be proved to be true (or false), but it is nevertheless justified by the success and predictive power of the theory that contains the concepts assumed to exist. I also claim that the ontological commitments one holds (even if tacitly so) have a great influence on what kind of science is produced, how it is used, and how it is understood. Not only I justify this claim, but I also propose a sketch of a possible falsification of it. As a natural conclusion, I defend the importance of identifying, clarifying and making explicit one's ontological commitments if fruitful scientific discussions are to be had. Finally, I compare my point of view with that of some philosophers and scientists who have put forward similar notions.Comment: Submitted for peer-revie

Some lessons for us scientists (too) from the "Sokal affair"

In this little non-technical piece, I argue that some of the lessons that can be learnt from the bold action carried out in 1996 by the physicist Alan Sokal and typically known as the "Sokal affair" not only apply to some sector of the humanities (which was the original target of the hoax), but also (with much less intensity, but still) to the hardest sciences

Reducing the standard deviation in multiple-assay experiments where the variation matters but the absolute value does not

You measure the value of a quantity x for a number of systems (cells, molecules, people, chunks of metal, DNA vectors, etc.). You repeat the whole set of measures in different occasions or assays, which you try to design as equal to one another as possible. Despite the effort, you find that the results are too different from one assay to another. As a consequence, some systems' averages present standard deviations that are too large to render the results statistically significant. In this work, we present a novel correction method of very low mathematical and numerical complexity that can reduce the standard deviation in your results and increase their statistical significance as long as two conditions are met: inter-system variations of x matter to you but its absolute value does not, and the different assays display a similar tendency in the values of x; in other words, the results corresponding to different assays present high linear correlation. We demonstrate the improvement that this method brings about on a real cell biology experiment, but the method can be applied to any problem that conforms to the described structure and requirements, in any quantitative scientific field that has to deal with data subject to uncertainty.Comment: Supplementary material at http://bit.ly/14I718

Tendency in the starting and corrected results.

<p>Variation of the quantity (MetLuc activity) in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0078205#pone-0078205-t001" target="_blank">table 1</a> for the six systems (vectors) studied, before and after the correction described in The method. Each color corresponds to a different assay, and the lines joining the experimental points have been added for visual comfort.</p

Starting and corrected results.

<p>Starting and corrected results.</p

Errors in the starting results.

<p>Bar chart representation of the average values (orange bars) and the associated standard deviation (black capped lines) in tab. 1.</p

p-values associated to the starting results.

<p>p-values associated to the starting results.</p