Science is a heavily vetted and reviewed field. It has to be to make sure that the science out there is accurate and trustworthy. When dodgy science appears, the scientific community are often quick to question it, perhaps point out its inaccuracies, and replace it with better research. This whole process involves a whole lot of thought and debate- is it accurate? If not why? What do we count as being accurate or true? Why do we do science the way we do it? Perhaps surprisingly this all boils down to philosophy.
Scientific research is lead by a few different philosophical ideas or ways of questioning, some which are complementary and some which are occasionally opposing. This week we will be taking a look at some of these philosophies, from logic to bias via a Franciscan friar who liked simplicity. It’s a bit dense, I’m not going to lie, but it can be an interesting way to reflect on how any of us, scientists or otherwise, choose to believe things as logical/true/accurate or not. Enjoy!
Logic- deductive and inductive
We’re gong to examine some of the ways we think and determine what we think is true, starting with logic. Generally in logic there are two broad ways of deciding what makes sense- deductive and inductive logic. In deductive logic you might say all people are mortal, and Sally is a person, therefore Sally is mortal. You know that mortal=people and people=Sally, so Sally must be mortal. This deductive logic uses examination or elimination of rules, like problem solving, and is a very useful way of understanding something better if you already have a basic understanding of it, i.e. if you know some of the rules. This is especially used in fields like maths.
Inductive logic works the other way, observing trends and then creating theories from them, for example the sun has come up every day, so we infer it will come up again tomorrow, or this drug had a positive effect on 100,000 cells in a body, so we infer it will have a positive effect on all those kinds of cells. This kind of logic is more useful when you don’t know all the rules surrounding something for absolutely certain, or you don’t want to spend all you time repeating tests.
These two forms of logic are both very useful in their own ways, and are how we know what we know about the world. However, a word of caution, they are not fool-proof. For example if we know all boys like oranges, and Sam likes oranges, deductive logic would tell us Sam is a boy. However, Sam(antha?) might not be a boy, as just because all boys like oranges it doesn’t mean a girl can’t like them too. The same goes for inductive logic, as we might say that because all the swans we have seen are white, swans must all be white. However, if you go to Australia there is actually black swans. When we do research, we can’t check everything e.g. it would be a waste of time and energy checking that the sun rises every morning to be sure that it will rise again, or to test one drug on literally every cell in existence, it just does run the very slight risk that we might have missed an important observation to disprove our theory, like a black swan.
These caveats and notes of warning aside, deductive and inductive logic do work very well for most situations.
Causality, Correlation and Occams Razor
The next thing to consider is causality and correlation. Causality is a relationship between two things where one causes the other- for example if you have more money you tend to have a bigger house (because you have the money to buy a bigger house), or if you start skiing younger you tend to be better at skiing (because you’ve had more time to learn) etc. These are pretty simple relationships, and are often the conclusion that we jump to when we see a graph with a line going diagonally up with two factors. However, it is important to be aware that these relationships might also just be correlations.
A correlation is a positive relationship between two things but where one might not cause the other. For example people living closer to a city centre might be richer than those living in the outskirts, but that doesn’t mean if you move a random person into a city centre they will magically become rich. It is likely that housing is more expensive in the centre so only wealthy people can afford to live there, whilst those on a lower income can only afford houses on the outskirts. Yes, there is a relationship between city area and wealth, but the city area doesn’t cause the wealth, so this is correlation not causation. It can be good when presented with graphs or data to take a moment to think whether the relationship is one of causation (a causes b) or correlation (a and b are connected, but don’t cause one another).
When you only have a correlation, that’s when you can come up with an alternative hypothesis or theory on what is happening, like above I suggested maybe people are poorer in the outskirts of a city because housing is cheaper, so only those on a lower income can afford to live there. When coming up with alternative reasonings or hypothesis for a relationship, you can go wild and have millions. To decide which is most likely, ‘Occams razor’ is often used, names after a Franciscan Friar named William of Ockham who used it in England in the 1200s. This is a tool in philosophy which basically says that the explanation with the fewest number of assumptions is most likely true. For example, you might assume someone is short because of genetics, or alternatively they are short because as a child they did a lot of manual labour. The first explanation assumes they are genetically predisposed to being short. The second explanation assumes when they were young they did manual labour, and that manual labour as a child makes you short, and that they did enough manual labour for it to make them short. According to Occams razor, the first explanations has the fewest assumptions so that’s the one to go with. If they then tell you that they did indeed do lots of manual labour as a child, or that their parents are actually tall, your assumptions change and you reassess with your new data.
Some other forms of thought
There’s a number of other philosophical schools of though worth mentioning but which I’ll fly by. We have empiricism and rationalism. Empiricism is the reliance on our senses and on measurements to understand things, whilst rationalism relies on deduction and logic. Again, both are useful in their own way, and often both are used in research together. Rationalism might make an isolated island community think- this island has people, so other islands might have people too. Keeping this in mind, later the following year some rubbish not from their island floats by. Empirical thought, using the backing of the rational assumption, can say this gives evidence other people live on other islands as we have observed it. Another example is maths. Think back to high school classes on Pythagoras where we learned that in a right angled triangle the square of side a added to the square of side b equals the square of side c. Using this mathematical equation to infer the length of side c is rationalism, whilst just measuring it would be empiricism.
Then we have hypothesis lead research vs observational research. Hypotheses are ideas to prove or disprove. So you might have the hypothesis that belugas only swim in groups. Then you set out to study beluga behaviour and either confirm your hypothesis (yup, they’re a social bunch) or reject your hypothesis (nope, they’re loners). Observational research on the other hand doesn’t test a question or assumption, it simply goes out and observes. Then after these observations, e.g. seeing that a bird does a special dance at 10am every morning, it can come up with explanations, e.g. it is a mating ritual and that’s when females are most likely to be nearby. This is then usually followed by hypothesis led tests.
The final one worth mentioning is confirmation bias. The whole purpose of science is to be detached- to test and observe things with no expectations and to faithfully and accurately report back the results, no matter what they are. There are a lot of rules and processes in science to ensure that it is trustworthy, detached and accurate. However, science is carried out by humans, not robots. Even if robots are involved in the data collection, the humans are the ones interpreting it at the end of the day. That is why scientists, and everyone, need to be very aware of confirmation bias, which is wanting or expecting to see a specific result so much that we unconsciously focus more on the information that supports our views and forget the info that doesn’t. We all do this to some degree or another, seeing things as we want to see them or filtering information out that we don’t like. For scientists, it is extra important to be conscious and try to avoid this confirmation bias. As I said, it is often caught at some point in the research/publishing process, but better to not be biased in the first place than to awkwardly get called out for it.
Whilst science and philosophy might seem at totally different ends of the spectrum, they are actually very closely intertwined. Some of the earliest scientists, like Aristotle, were philosophers and even today the scientific data we collect is filtered through the scientific world’s philosophies on what is true, logical or accurate. None of these methods of thought are perfect, or apply well to every research question, so understanding them well is important so we can use the right philosophical tool for the right scientific job. Not only do these ways of thinking affect science, but they are things that we all use or don’t use in our everyday life to make sense of the world, and can be interesting for all of us to reflect on. As always, this has been a whistle stop tour of quite a large topic, so if you’re interested in learning more I recommend looking at some the links below or going down an internet rabbit hole on the subject.
For more info:
A video on everyday confirmation bias, especially in relation to fake news:
A video about Plato and Aristotle, early scientific philosophers. It covers some of they ways they saw the world, ways which we know now to be wrong, yet which were based on their logic, showing how important it is to be aware of what kind of logic we are using and why:
Cover Image: University of Utah