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.

References

- 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