The diversity of viewpoints and positions around all study topics is very important in the science world. This variety thoughts allows to evaluate different perspectives of the same problem using different methodologies, and generate a great framework (Pavese and De Bièvre, 2015). Nevertheless, science must go hand in hand with objectivity (Hanna, 2004), therefore we must ask ourselves, which is the best approach to carry out scientific research?
Firstly, we must located on a radical idea: Falsification (Popper, 2002a). For this Popperian vision we only know what we don’t know, thus, being literals, it’s impossible discover absolutes trues, and we can only corroborate and falsify hypotheses (Popper, 2002b). My viewpoint about Popper’s falsification in science is: we must use it only as a reminder of why we shouldn't take for granted the current theories, thus, the search of knowledge remains standing. However, beyond this last idea, I don’t think Popper’s thought has a direct applicability nowadays.
Now, we can understand that the impulse of science is to understand more and more things, but, how do we trust what we know? The knowledge search through science needs statistics, and frequentists with their classical interpretation of probability based on finite observations of experimental events, have contributed greatly (Bayarri and Berger, 2004). Nevertheless, this vision can generate problems like wrong interpretations or the impossibility of assign probabilities to unrepeatable events (Bayarri and Berger, 2004; Box and Tiao, 1992). Moreover, Bayesian vision doesn’t have these problems being a better option because probabilities are based on a prior knowledge, it means, there is always uncertainly because we never know all facts but we can assign a value of how much knowledge have about results (Briggs, 1999; Schoot et al., 2014).
For this reason, I must emphasize that we can’t know everything but we can know a lot, therefore, methods based on the Bayesian philosophy, give us, in my opinion, the best approximation to the scientific truth, especially in systematics, where the evolutionary history is a large set of inferences.
- Bayarri, M. J. and Berger, J. O. (2004). The interplay of Bayesian and frequestist analysis. Statistical Science, 19(1), 58-80.
- Briggs, A.H. (1999). A Bayesian approach to stochastic cost‐effectiveness analysis. Health Econ., 18, 257-261. doi:10.1002/(SICI)1099-1050(199905)8:3<257::AID-HEC427>3.0.CO;2-E
- Box, G. E. and Tiao, G. C. (1992). Bayesian inference in statistical analysis. New York: John Wiley & Sons.
- Hanna, J. (2004). The Scope and Limits of Scientific Objectivity. Philosophy of Science, 71(3), 339-361. doi:10.1086/421537
- Pavese, F. and De Bièvre, P. (2015). Fostering diversity of thought in measurement science. In F. Pavese, W. Bremser, A. Chunovkina, N. Fischer and A. Forbes (Ed.), Advanced Mathematical and Computational Tools in Metrology and Testing X (pp. 1–8). Singapore: World Scientific
- Popper, K. (2002a). The logic of scientific discovery. London, England: Routledge. doi:https://doi.org/10.4324/9780203994627
- Popper, K. (2002b). Conjectures and refutations: The growth of scientific knowledge. London, England: Routledge. doi:https://doi.org/10.4324/9780203538074
- Schoot, R., Kaplan, D., Denissen, J., Asendorpf, J. B., Neyer, F. J. and Aken, M. A. (2014). A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research. Child Dev, 85, 842-860. doi:10.1111/cdev.12169