For our second Communicating Science class discussion, we looked into the quantitative methodology we so often use in science!

The class discusseed Francis Bacon’s “Novum Organum: True Directions Concerning the Interpretation of Nature” to lay the foundation.

Here’s why.

After Aristotle, deductive reasoning was prominent: “Take what we know and try to expand on it.”

But the Renaissance of the 1400s introduced inductive reasoning that Bacon would build his argument on: “Set aside what we know and make sure to establish basic facts.”

[More on Aristotle in this previous thread.] https://twitter.com/srmullens/status/1352112256004325379

The Protestant Reformation in the 1520s then set quite a precedent of making it acceptable to question established institutions.

Protestants questioned the Catholic church. Bacon would question the institution of deductive reasoning.

Francis Bacon’s “Novum Organum” to science what Martin Luther’s “95 Theses” was to the church.

To paraphrase, Bacon basically said:

1) We’ve solely focused on what we believe has a practical use. But that’s how we got alchemy, folks! Fundamental knowledge is important.

2) We favor the knowledge of antiquity and the arguer’s age and status over data. This is despite rhetoricians occasionally acknowledging the world is nuanced and complex beyond our understanding!

3) In spite of #2, we prefer to abstain from pursuing difficult tasks, make assumptions about what we don’t know, and say exceptions are the fault of nature rather than of our theories.

4) In reality, we don’t know what we are looking for. We don’t have a good method for making discoveries. We haven’t really made that much scientific progress since Aristotle.

5) We got here by not incentivizing data, not incentivizing creativity, not rewarding success, and lacking hope of discovering what we don’t know.

How do we do better? Bacon proposed experimentation and the scientific method! (also, this is straight )

But what did we end up with? As communicators, it’s important to recognize what we are assuming as we go about our now common quantitative scientific methodologies.

Let’s state and discuss some assumptions:
1) "Experiments are the way to truth.” You’re thinking: “Word." But we have to acknowledge that this is easier for some areas of study (physics, chemistry) than others (anthropology, economics). [This will be a pattern.]

2) "Experiments should be systematic and repeatable."

The hard sciences be like, “Word.”

Scientists studying people be like, “I promise you I’m trying.”

Physicists want to isolate their experiments from any outside influences. Instruments have been created to observe and measure. Units have been standardized. Researchers can get famous for creating experiments, but they should be repeatable by anyone with the resources.

But the bigger the experiment, the harder it is to isolate your experiment from outside influences. The more the experiment involves people, the more ethical questions arise as you try.

3) "Math helps quantify physical phenomena and detect patterns.” If you are lucky enough have large sample sizes, math is undeniably helpful. This is how we get fundamental, baseline knowledge and detect erroneous hypotheses.

But what if the thing you study is rare? Or expensive to observe? Or it’s only been observed onece? What if the context where it’s been observed is highly variable? What if your presence affects the outcome? Math can’t rescue you!

4) "Change should have a direct physical cause." Again, this is more true in some situations than others. You can discover patterns of cause and effect forever, but no measurement or statistic can translate that to answer questions of “why.”

Communicating your science is obviously going to start with the conclusions we know. But the more detailed the questions you get asked, the more you’re going to have to get into the methodology of experiments, and maybe even the assumptions of quantitative methods itself! /Fin