(For an example of real science in action, watch the video)
In the last few lessons we’ve looked at 5 common argument schemes: Generalizations, polling, general causal reasoning, particular causal reasoning, and arguments from ignorance. As luck would have it, these are the most common argument schemes you will find in (good and bad) scientific arguments. Arguments are important to the scientific enterprise because a core activity of science is to provide reasons and evidence (i.e., arguments) for why one hypothesis should be accepted over another. This question of why we should chose one hypothesis over another (or any hypothesis at all) brings up many interesting philosophical issues which (time permitting) we will briefly explore. However, before putting on our philosopher hats, lets put on our lab coats, turn on our bunsen burners and take a closer look at the scientific method.
We can break up the scientific method into 5 steps:
Step 1: Understanding the Issue
In this first step, the goal is simply to determine what it is exactly that we want to know. Usually, it will be a problem that we want solved. Examples might include, what is the mass of an electron? Can vaccines prevent measles? Can Tibetan monks levitate? Is the earth round? Can wi-fi cause health problems? Does the color red make people feel hungry? How do magnets work? Does honey diminish the severity of coughs?
As you can see some of these issues will involve questions about causation while others might be about identifying something’s properties.
Step 2: Formulating a Hypothesis
In the next step, we want to formulate a hypothesis that will solve our problem and the hypothesis must be testable (recall that a non-testable hypothesis is non-falsifiable and thus considered pseudo-scientific).
To illustrate how this works, lets consider the problem of whether honey diminishes the severity of coughs. Our basic hypothesis will be “honey diminishes the severity of coughs.”
However, often our hypothesis will extend beyond a simple “yes” or “no”. We will want to know why it does or doesn’t have a particular effect on a cough. This is known as the “causal mechanism”; i.e., the thing that causes the effect that our hypothesis anticipates. So, if honey diminishes the severity of coughs, we will want to know why. If we don’t know why then it may simply be correlation. We are trying to establish causation. Maybe it’s the tea we drink the honey with that causes the diminished severity. Or maybe it isn’t honey itself that causes the reduced severity maybe it’s the sugars in honey and so any sweet substance will do.
Part of establishing causation is to rule out competing hypothesis. So, if someone says that honey diminishes the severity of coughs because the sweetness in honey activates some particular receptor cells that in turn help diminish the severity of the cough, then we can test that. Someone else might say it’s because the honey reduces swelling in the throat. We can test that a too. Or someone else might say honey has some anti-bacterial or anti-viral compounds which kill the bacterial/viral cause of the cough.
The point is, we need to pick a hypothesis that (preferably) is also specific enough to also include a causal mechanism. Lets choose the first one.
Hypothesis (h): Drinking honey can reduce the severity of coughs.
Causal Mechanism: h because
“the close anatomic relationship between the sensory nerve fibers that initiate cough and the gustatory nerve fibers that taste sweetness, an interaction between these fibers may produce an antitussive effect of sweet substances via a central nervous system mechanism.”
Fallacy Alert! Aruga! Aruga! In scientific debates it’s very important to hold your opponent to their hypothesis (and also to keep to yours when facing objections or contravening evidence). Changing the hypothesis mid-debate is called moving the goal posts. This is a very common practice among purveyors of pseudo-science or members of the anti-science ideologies.
For example, for years anti-vax groups opposed vaccines because–they hypothesized–thimerisol causes autism. Because this myth became so pervasive (despite overwhelming evidence to the contrary) and in order to ensure compliance rates high enough for herd immunity, many national health departments changed to the more expensive thimerisol-free versions of the vaccines. Contrary to the anti-vax hypothesis, removal of thimerisol from vaccines was followed by autism rates actually going up rather than down! (There’s some weak evidence to suggest that vaccines can actually inhibit some kinds of autism).
Now that thimerisol is removed from vaccines and the anti-vax hypothesis has been proven to be empirically false, what do you think the response of the anti-vax crowd is? If you guessed, “oh, lets support vaccines now,” you were sleeping for the 2 weeks of this class! The response was to “move the goal posts.” Now it’s “too many too soon!” or “it’s got aluminum in it!” or “it’s got mercury in it!”.
Step 3: Identifying the Implications of the Hypothesis
In the next step we need to set out our expectations for what we’d expect to see (i.e., observations) if your hypothesis is correct. It’s very important that this is done before the experiments are conducted. In the case of the honey, we’d expect to see that (a statistically significant number of) people who have a cough will cough less frequently and violently then a comparable group of people with a cough but who don’t take honey (or any other “medicine”). In the case of thimerisol, we might say, if it’s true that thimersol causes autism, then when we remove thimersol from vaccines we should expect to see autism rates decline.
We can formalize this structure:
If the hypothesis (h) is true, then x will occur. (x is our expected observable outcome).
So, in the case of honey, if our hypothesis is true, then those who drink honey will have reduced severity of coughing compared to a control group.
Step 4: Testing the Hypothesis
As you might expect, once we’ve set up our hypothesis and established the anticipated observable effects that would confirm the hypothesis, we test!
Recall step 2: when we form the hypothesis, we should ensure that the hypothesis is testable. That is to say, that we can say in advance what will constitute observable confirmation or disconfirmation of the test. A couple of notes on why we must do this in advance. (1). This prevents retrofitting the data to fit the hypothesis; (2). if prevents the “moving of the goal posts”.
Testing in Principle vs Testing in Practice
Finally, we should be aware that not all hypotheses will in practice be testable, but they must be so in principle. For example, we can construct a hypothesis of what will happen if a large asteroid hits the earth but we don’t need to actually destroy half the earth to confirm the hypothesis that such an impact will indeed destroy half the earth. In some cases, running a computer simulation will do!
Step 5: Reevaluating the Hypothesis
In step 4, I emphasized that the predicted confirmatory results of the hypothesis must be made in advance to avoid retrofitting and moving the goal posts. However, this does not mean that once we have conducted a test that we can’t modify the test or the hypothesis. This is perfectly legitimate but must be done in a way that recognizes the shortcomings of the original test and/or hypothesis.
Fallacy Alert! Aruga! Aruga! Aruga! When the implications of our hypothesis are confirmed we must be careful not to immediately conclude that our hypothesis is confirmed. From the fact that our anticipated effect occurred it doesn’t necessarily follow that our hypothesis is true. This is called the fallacy of affirming the consequent, which looks like this:
P1 If h, then x. (in fancy talk, h is called the anticedent and x is called the consequent)
P2 x occurred.
C Therefore, h is true.
To see why h doesn’t necessarily follow, given that P2 is true (i.e., “affirming” the consequent), consider the follow case.
P1 If it’s raining, it’s cloudy.
P2 It’s cloudy.
C Therefore, it’s raining.
Just because it’s cloudy doesn’t mean it’s raining. It can be cloudy without it being rainy. It can also be partially cloudy with chances of sunshine in the evening, followed by overcast skies at night… you get the point.
In relation to scientific hypothesis we can imagine the following scenarios: Someone suggests a hypothesis h and anticipates a certain observable consequence x. But does it follow that just because x occurred that the hypothesis is true? Nope. There are many possible alternative reasons (or causes) besides h for which x might have occurred.
If we think back to the sections on general causal reasoning we can see why. If the hypothesis is a causal one, then there are several steps we need to go through before we can attribute causality. Maybe there’s only a statistical relationship between two variables? (correlation) Maybe, there’s some other better explanation (h) for why x is occurring? Maybe the methodology was flawed. (No double blinding=placebo effect, problems with representativeness and sample size, etc…).
Summary: Steps of the Scientific Method
1. Understand the Problem that requires a solution or explanation.
2. Formulate a hypothesis to address the problem.
3. Deduce the (observable) consequences that will follow if the hypothesis is correct.
4. Test the hypothesis to see if the consequences do indeed follow.
5. Reevaluate (and possibly reformulate) the hypothesis.