Doubting is a good thing. Doubting with method is a better thing. - A method please ! - 2/3

Good morning, dear readers !

Last time I brought up the notion of doubt and showed you differences between relativism and (philosophic/scientific skepticism). Now, let me take you to a trip throughout the scientific method.

The Scientific Method

-A tiny disclaimer :

Firstly, I have to admit something to you my lovely reader, the scientific method IS NOT perfect. It is just the best one that we have NOW to produce knowledge. The way of doing Science has been improved for several centuries and will continue (I hope so). Yet, some of the people fall into scientificism. In a nutshell, this notion describes the idea that Science is able to answer all of our problems. Well, to be honest, you should be aware that I am a little bit scientist, keep this in mind in this article. Nevertheless, I will do my best to give counterpoints and be as objective as I can.
Whatever, Science is descriptive, not normative.

I will not discuss about the scientificism anymore and I give my voice to the French economist Jean Fourastié: “Science tells how we are here, it does not teach us why we are, where we go, or what goals for our life and societies that we have to follow.”

-End of disclaimer.

In fact, scientific method gathered lot of models of scientific inquiry. However, there is a most used one.

Hypothetico-deductive model:

A long and disgusting word for something very easy. Here the step to use the famous 
Hypothetico-deductive model: 

1) I formulate a hypothesis which is falsifiable (i.e. I can answer either YES or NO). The falsifiability criteria comes from the Karl Popper (1902-1994).

2) I write a protocol to make an experience in order to invalidate my hypothesis,

3) I make my experience and compare obtained data to my hypothesis,

4) If my data invalidate my hypothesis, I reject it. Otherwise I’m just able to claim that my hypothesis can’t be invalidate with my experience. Indeed, maybe my material, my manipulation, etc. were not good enough.

NB: We call this kind of reasoning: “Modus Ponens”: [Data  ( ∧ Data  ⇒ Hypothesis not rejected)]  ⇒ Hypothesis not rejected.
 
Careful, before wearing your lab coat and publishing in Nature I should have a word to say about Robustness.

Robustness:  

Some definitions:

1) “Repetition, variance and multiple-determination function as methodological criteria for scientific methods that justify the acceptance of epistemological and ontological results.”-1
2) “Reproducibility and stability function as ontological criteria for the acceptance of phenomena described by A→B.”-1

You can see with those quotations that two notions seems to be very important. In order to make those concepts very clear for you, we will use an example. First of all, repetition.
I love bees and flowers, do you? 

Whatever, let my hypothesis being: 

1) When a bees is foraging a flower, it is also gathering pollen.

2) My protocol: I will look bees while they are foraging. If they have pollen with them, my hypothesis can’t be rejected. Otherwise, I reject my hypothesis.

3) I look one bee, it has not pollen.

4) Thus, my hypothesis is rejected.

Now, you are surely pointing your mouse on the red cross to close this blog. I understand. Notwithstanding, I followed the Hypothetico-deductive model my thinking was right? But where is the main problem? Repetition of course! 

Repetition:

Look at part 3: “I look one bee, it has not pollen.” One bee is not enough. My bee may has not hair due to disease, malformation, etc. The more I consider bees, the more my errors due to outliers is minor. In other words, when I increase my sample size, my margin of error decreases-2. Here we are, large sample size is equivalent to high number of repetitions of the experience. At last, remember that there is always a margin of error between experimental data and theory.

Reproducibility:

Henceforth, another issue exists. Despite my sample size is large, I could have regional variations which has an important effect on the bees that I am studying. So as to correct that issue (but not only), scientists reproduce their experience many times and all around the world. Here is the reproducibility.

Peer review:

Also, there is another subject that I want to talk about; Peer review (provided by Henry Oldenburg (1619–1677).
In plain English, once you righted an article you have to send it to your editor. Your paper will be submitted to scientific expert in your field. The latter assess your work, make you recommendations and you correct your paper. Thus, if your article is good, it is published into a scientific review.
In conclusion, we have an experience with less bias than the beginning. However, keep in mind that we still have bias in every experiences.

A last and very important notion: CONSENSUS:

Definition: “Scientific consensus is the collective judgment, position, and opinion of the community of scientists in a particular field of study. Consensus implies general agreement, though not necessarily unanimity.”-4

In relation to the definition above, if you have a theory which goes against the scientific consensus you will need very strong proofs.
In other words; "And when such claims are extraordinary, that is, revolutionary in their implications for established scientific generalizations already accumulated and verified, we must demand extraordinary proof." - Marcello Truzzi (1935-2003) .

After this stop at the scientific method station, our terminus will be in Zététique world. See you soon !

Sketpically.

Erwan Meunier.

Sources :

1 - Boon, M. (2012). Understanding scientific practices: The role of robustness notions. In L. Soler (Ed.), Characterizing the robustness of science : after the practice turn in philosophy of science (pp. -). (Boston studies in the philosophy of science; Vol. 292, No. 292). Dordrecht: Springer. https://doi.org/10.1007/978-94-007-2759-5_12

2 - Wikipedia contributors. (2020, April 8). Law of large numbers. In Wikipedia, The Free Encyclopedia. Retrieved 08:48, April 10, 2020, from https://en.wikipedia.org/w/index.php?title=Law_of_large_numbers&oldid=949793784 

3 - Kuhn, Thomas S. (1962). The Structure of Scientific Revolutions (1996 ed.). University of Chicago Press, Chicago. ISBN 978-0-226-45808-3. 

4 - Wikipedia contributors. (2020, April 8). Scientific consensus. In Wikipedia, The Free Encyclopedia. Retrieved 08:28, April 10, 2020, from https://en.wikipedia.org/w/index.php? title=Scientific_consensus&oldid=949783831 

5 - Marcello Truzzi. « Éditorial », The Zetetic, vol. 1, no. 1, fall/winter 1976, p. 4