10B 2. (a) (b) (c)
11A (a) (b) (c)
Wednesday, November 27, 2013
Tuesday, November 26, 2013
Slippery Slope Arguments
Introduction
In the previous posts we looked at argument schemes that are typically employed in factual matters: generalizations, polls, general causal reasoning, particular causal reasoning, and the argument from ignorance. In this next section we'll look at common argument schemes used in normative (i.e., having to do with values) arguments. Check. it. aus...
Slippery-Slope Argument
A slippery slope argument is one where it is proposed that an initial action will initiate a causal cascade ending with a state of affairs that is universally desirable or undesirable. The implication is that we should (or should not) do the initial action/policy because the cascade of events will necessarily occur.
A contemporary example of a negative version of the slippery slope argument comes from arguments against gay marriage equality. Some opponents argue that if same sex couples are allowed to marry, then there will eventually be no good reasons against people marrying animals and so society will have to permit this too.
(Yes, people actually make this argument...can I marry my horse? )
Here's a good clip were the slippery slope argument is mentioned explicitly: Video of O'Reilly factor slippery slope argument. Start at 2:00
A positive version of the slippery slope argument might be something like a Libertarian argument (over-simplified version): We should treat the principle of self-ownership as the primary governing principle, if we do, then you will remove taxation and government, then the market will cease to be distorted and people will act in their own self-interest, and people acting in their own self-interest will pull themselves up by their own bootstraps, a society of self-pulled up people will have relatively few social problems thus eliminating many of the existing ones. (Note: We could do an over-simplified version of just about any political philosophy and show it to be weak)
So, why are these arguments not very strong? To figure it out, lets look at the underlying structure of a slippery slope argument. Recall that a slippery slope argument is one where it is supposed that one initial event or policy will set off an necessary unbroken sequence of causal events.
If we formalize it, it will look like this:
P1: If A then B.
P2: If B, then C.
P3: If C then D.
P4: If D, then E.
P5: If E, then F.
P6: (So, if A then F)
C F is a good thing, therefore we should do A. (Positive conclusion).
C* F is a bad thing, therefore we should't do A. (Negative conclusion).
(We can also condense P1-P5 as a single compound premise: If A then B, if B then C, If C then D,...)
If we think back to the lecture/post on principles of general causal reasoning we will recall that it takes quite a bit of evidence to say that even a single causal argument is good (e.g. If A then B). As you might imagine, the longer the causal chain gets, the more difficult it will become to ascertain that the links along the way are necessarily true and not open to other possible outcomes.
Returning to our examples, in the first case, one of the causal elements has to do with the equivalence between reasons against gay marriage and reasons against animal marriage. It doesn't take much imagination to come up arguments for why the two types of prohibitive reasons aren't the same (capacity for mutual informed consent for starters...). Showing that there is a relevant distinction between the types of reasons causes a break in the causal chain, thereby rendering the argument weak.
In our-simplified version of the Libertarian argument relies on a long causal chain that begins with the primacy of the self ownership principle and reduced taxation and government and ends with a decrease in prevailing social problems. Along the way there are may suspect causal claims that individually might not stand too much scrutiny--especially since many of the claims are hotly debated by experts in the respective fields. Since a chain is only as strong as its weakest link, this will have a detrimental effect on the overall strength (logical force) of the conclusion.
Upshot: So, what's the overall status of slippery slope arguments? Just like many argument schemes they can be both strong or weak depending on their constituent parts. In the case of slippery slope arguments, a strong one will have highly plausible causal claims all linked together, culminating in a glorious well-supported conclusion about what we should or should not do. Conversely, a weak slippery slope argument will have one or more weak causal claims in its implied premises.
In the previous posts we looked at argument schemes that are typically employed in factual matters: generalizations, polls, general causal reasoning, particular causal reasoning, and the argument from ignorance. In this next section we'll look at common argument schemes used in normative (i.e., having to do with values) arguments. Check. it. aus...
Slippery-Slope Argument
A slippery slope argument is one where it is proposed that an initial action will initiate a causal cascade ending with a state of affairs that is universally desirable or undesirable. The implication is that we should (or should not) do the initial action/policy because the cascade of events will necessarily occur.
A contemporary example of a negative version of the slippery slope argument comes from arguments against gay marriage equality. Some opponents argue that if same sex couples are allowed to marry, then there will eventually be no good reasons against people marrying animals and so society will have to permit this too.
(Yes, people actually make this argument...can I marry my horse? )
Here's a good clip were the slippery slope argument is mentioned explicitly: Video of O'Reilly factor slippery slope argument. Start at 2:00
A positive version of the slippery slope argument might be something like a Libertarian argument (over-simplified version): We should treat the principle of self-ownership as the primary governing principle, if we do, then you will remove taxation and government, then the market will cease to be distorted and people will act in their own self-interest, and people acting in their own self-interest will pull themselves up by their own bootstraps, a society of self-pulled up people will have relatively few social problems thus eliminating many of the existing ones. (Note: We could do an over-simplified version of just about any political philosophy and show it to be weak)
So, why are these arguments not very strong? To figure it out, lets look at the underlying structure of a slippery slope argument. Recall that a slippery slope argument is one where it is supposed that one initial event or policy will set off an necessary unbroken sequence of causal events.
If we formalize it, it will look like this:
P1: If A then B.
P2: If B, then C.
P3: If C then D.
P4: If D, then E.
P5: If E, then F.
P6: (So, if A then F)
C F is a good thing, therefore we should do A. (Positive conclusion).
C* F is a bad thing, therefore we should't do A. (Negative conclusion).
(We can also condense P1-P5 as a single compound premise: If A then B, if B then C, If C then D,...)
If we think back to the lecture/post on principles of general causal reasoning we will recall that it takes quite a bit of evidence to say that even a single causal argument is good (e.g. If A then B). As you might imagine, the longer the causal chain gets, the more difficult it will become to ascertain that the links along the way are necessarily true and not open to other possible outcomes.
Returning to our examples, in the first case, one of the causal elements has to do with the equivalence between reasons against gay marriage and reasons against animal marriage. It doesn't take much imagination to come up arguments for why the two types of prohibitive reasons aren't the same (capacity for mutual informed consent for starters...). Showing that there is a relevant distinction between the types of reasons causes a break in the causal chain, thereby rendering the argument weak.
In our-simplified version of the Libertarian argument relies on a long causal chain that begins with the primacy of the self ownership principle and reduced taxation and government and ends with a decrease in prevailing social problems. Along the way there are may suspect causal claims that individually might not stand too much scrutiny--especially since many of the claims are hotly debated by experts in the respective fields. Since a chain is only as strong as its weakest link, this will have a detrimental effect on the overall strength (logical force) of the conclusion.
Upshot: So, what's the overall status of slippery slope arguments? Just like many argument schemes they can be both strong or weak depending on their constituent parts. In the case of slippery slope arguments, a strong one will have highly plausible causal claims all linked together, culminating in a glorious well-supported conclusion about what we should or should not do. Conversely, a weak slippery slope argument will have one or more weak causal claims in its implied premises.
Links from Lecture on Arguments from Ignorance
Link to Unexplained Escape video
Mumbai Weeping statue
Miracles graph
Pumapunku (Arg from personal incredulity, false dichotomy, arg from ignorance, arg from unqualified authority)
3:20-4:50, 5:10-5:35, 6:10-6:35, 7:30-8:00, 8:40, 12:20, 23:20-24:10
25:35 (gateway) 27:27-28:20
Puma punku AA debunk
Arguments from Ignorance
Introduction
The next argument scheme we will look at is what's known as the argument from ignorance. An argument from ignorance (or argumentum ad ignorantium if you want to be fancy) is one that asserts that something is (most likely) true because there is no good evidence showing that it is false. It can also be used the other way to argue that a claim is (most likely) false because there's not good evidence to show that it's true.
Lets look at a couple of (valid) examples:
There's no good evidence to show that the ancient Egyptians did have digital computers. (This evaluation comes from professional archeologists), therefore, they likely didn't have digital computers.
Or
There's no good evidence to suppose the earth will get destroyed by an asteroid tomorrow. (This evaluation comes from professional astronomers), so we should assume it won't and plan a picnic for tomorrow.
Or
There's no good geological evidence that there was a world-wide flood event. (This evaluation comes from professional geologists); therefore, we should assume that one never happened.
Formalizing the Argument Scheme
As you may have guessed, we can formalize the structure of the argument from ignorance:
P1: There's no (good) evidence to disprove (or prove*) the claim.
P2: There has been a reasonable search for the relevant evidence by whomever is qualified to do so.
C: Therefore, we should accept the claim as more probable than not/true.
C*: Therefore, we should reject the claim as improbable/false.
Good and Bad Use of Argument from Ignorance
The argument from ignorance is philosophically interesting because sometimes the same structure can be used to support the opposing position. The classic example is the debate over the existence of God. Lets look at how both sides can employ the argument from ignorance to try to support their respective position.
Pro-God Arg
P1: You can't find any evidence that proves that God or gods don't exist.
P2: We've made a good attempt to find disconfirming evidence, but can't find any!
C: Therefore, it's reasonable to suppose that God or gods do exist.
Vs God Arg
P1: You can't show any evidence that God or gods do exist.
P1*: Any evidence you present can also be explained through the natural laws.
P2: We've made a good attempt at looking for evidence of God's/gods' existence but can't find any! (I even looked under my bed!)
C: Therefore, it's reasonable to suppose that God/gods don't exist.
This particular case brings out some important issues we studied earlier in the course such as bias and burden of proof. Not surprisingly, theists will find the first argument convincing while atheists will be convinced by the latter. This of course brings up questions of burden of proof. When we make a claim for something's existence, is it up to the person making the claim to provide proof? Or does the burden of proof fall on the critic to give disconfirming evidence? In certain questions, your biases will pre-determine your answer.
While in the above issue, there is arguably reasonable disagreement on both sides, there are other domains where the argument from ignorance fails as a good argument. As you might guess, this will have to do with the acceptability of P1 (i.e., there is/is no evidence) and P2 (i.e., a reasonable search has been made). Most criticism of arguments from ignorance will focus on P2--that the search wasn't as extensive as the arguer thinks. Generally, we let P1 stand because it is usually an authors opinion to the best of their own knowledge. Recall from the chapter on determining what is reasonable, we typically let personal testimony stand.
We can illustrate a poor example of an argument from ignorance with an example. Claim: There's no evidence to show Obama is American, therefore he isn't American.
Lets dress the argument to evaluate it:
P1: I've encountered no good evidence to show that Obama is an American citizen.
P2: Numerous agencies and individual trained in the search and identification of state documents have been unable to locate any relevant documents.
C: Obama isn't American (and is a Communist Muslim).
Regarding P1, maybe the arguer hasn't encountered any evidence so we'll leave it alone. P2 however has problems. There have been reasonable searches for evidence, and that evidence was found. Perhaps, the arguer was unaware or didn't truly exert him/herself enough. The argument fails because P2 is not acceptable (i.e., false).
We can also typically find the argument from ignorance used in arguments against new (or relatively new) technologies in regards to safety or efficacy. For example:
We should ban GMOs because we don't know what long-term health effects are.
Dressed:
P1: I've found no evidence that shows that GMOs are safe for human consumption.
P2: Those qualified to do studies and evaluate evidence have found no compelling evidence to show that GMOs are safe for human consumption.
C: Therefore, we should assume GMOs are unsafe and ban them until we can determine they are safe.
If we were to criticize this argument we'd consider P2. In fact, there have been quite a few long term studies done by those qualified to assess safety. At this point we will have a debate over quality of evidence. Some on the anti-GMO side dispute the quality of the evidence (i.e., it was funded by company x, and therefore it is questionable). In a full analysis we'd consider this question in depth, but for our purposes here, we might legitimately challenge the claim that there is no available evidence purporting to demonstrate safety.
As an aside, notice that we can also use the argument from ignorance for the opposite conclusion: There's no compelling evidence to show that GMOs are unsafe for human consumption in the long-term, therefore, we should continue to make them available/ should not regulate them.
The "team" that wins this battle of arguments from ignorance will have much to do with our evaluation of P2: That there legitimately is or isn't quality evidence one way or the other.
Final Notes on Arguments from Ignorance
We can look at arguments from ignorance as probabilistic arguments. That is, given that there is little or no evidence for something, what is the likelihood that it still might exist? This is especially true for claims that something does exist based on an absence of evidence for its non-existence. However, as Carl Sagan famously said, "absence of evidence is not evidence of absence." In other words, just because we can't find evidence for something, doesn't mean that the thing or phenomena doesn't exist.
On the flip side, this line of argument can also be used to support improbable claims. Consider such an argument for the existence of unicorns or small teapots that circle the Sun: There's no positive evidence that unicorns don't exist or small tea pots don't circle the Sun, therefore we should assume they exist.
At this point we should return to the notion of probability: Given no positive evidence for these claims, what is the probability that they are true (versus the probability that they aren't)? It seems that, given an absence of evidence, the probability of there being unicorns is lower than the probability that they do not exist. Same goes for the teapot.
Typically, in such cases we say that the burden of proof falls on the person making the existential claim. That is, if you want to claim that something exists, the burden is upon you to provide evidence for it, otherwise, the reasonable position is the "null hypothesis." The null hypothesis just means that we assume no entity or phenomena exists unless there is positive evidence for its existence. In other words, if I want to assert that unicorns exist, using the argument from ignorance won't do. It's not enough for me to make the claim based on an absence of evidence. This is because, we'd expect some evidence to have turned up by now if there were unicorns (i.e., P2 of the implied argument would be weak).
This brings us to another Carl Sagan quote (paraphrasing Hume): "Extraordinary claims require extraordinary evidence." Or as Hume originally said: "A wise man proportions his beliefs to the evidence." Claiming that unicorns exist is an extraordinary claim and so we should demand evidence in proportion to the "extraordinariness" of the claim. This is why an ad ignorantium argument fails here; it doesn't offer any positive evidence for an extraordinary claim, only absence of evidence. We'll discuss this principle of proportionality more in the coming section. For now, just keep it in mind when evaluating existential arguments from ignorance.
The next argument scheme we will look at is what's known as the argument from ignorance. An argument from ignorance (or argumentum ad ignorantium if you want to be fancy) is one that asserts that something is (most likely) true because there is no good evidence showing that it is false. It can also be used the other way to argue that a claim is (most likely) false because there's not good evidence to show that it's true.
Lets look at a couple of (valid) examples:
There's no good evidence to show that the ancient Egyptians did have digital computers. (This evaluation comes from professional archeologists), therefore, they likely didn't have digital computers.
Or
There's no good evidence to suppose the earth will get destroyed by an asteroid tomorrow. (This evaluation comes from professional astronomers), so we should assume it won't and plan a picnic for tomorrow.
Or
There's no good geological evidence that there was a world-wide flood event. (This evaluation comes from professional geologists); therefore, we should assume that one never happened.
Formalizing the Argument Scheme
As you may have guessed, we can formalize the structure of the argument from ignorance:
P1: There's no (good) evidence to disprove (or prove*) the claim.
P2: There has been a reasonable search for the relevant evidence by whomever is qualified to do so.
C: Therefore, we should accept the claim as more probable than not/true.
C*: Therefore, we should reject the claim as improbable/false.
Good and Bad Use of Argument from Ignorance
The argument from ignorance is philosophically interesting because sometimes the same structure can be used to support the opposing position. The classic example is the debate over the existence of God. Lets look at how both sides can employ the argument from ignorance to try to support their respective position.
Pro-God Arg
P1: You can't find any evidence that proves that God or gods don't exist.
P2: We've made a good attempt to find disconfirming evidence, but can't find any!
C: Therefore, it's reasonable to suppose that God or gods do exist.
Vs God Arg
P1: You can't show any evidence that God or gods do exist.
P1*: Any evidence you present can also be explained through the natural laws.
P2: We've made a good attempt at looking for evidence of God's/gods' existence but can't find any! (I even looked under my bed!)
C: Therefore, it's reasonable to suppose that God/gods don't exist.
This particular case brings out some important issues we studied earlier in the course such as bias and burden of proof. Not surprisingly, theists will find the first argument convincing while atheists will be convinced by the latter. This of course brings up questions of burden of proof. When we make a claim for something's existence, is it up to the person making the claim to provide proof? Or does the burden of proof fall on the critic to give disconfirming evidence? In certain questions, your biases will pre-determine your answer.
While in the above issue, there is arguably reasonable disagreement on both sides, there are other domains where the argument from ignorance fails as a good argument. As you might guess, this will have to do with the acceptability of P1 (i.e., there is/is no evidence) and P2 (i.e., a reasonable search has been made). Most criticism of arguments from ignorance will focus on P2--that the search wasn't as extensive as the arguer thinks. Generally, we let P1 stand because it is usually an authors opinion to the best of their own knowledge. Recall from the chapter on determining what is reasonable, we typically let personal testimony stand.
We can illustrate a poor example of an argument from ignorance with an example. Claim: There's no evidence to show Obama is American, therefore he isn't American.
Lets dress the argument to evaluate it:
P1: I've encountered no good evidence to show that Obama is an American citizen.
P2: Numerous agencies and individual trained in the search and identification of state documents have been unable to locate any relevant documents.
C: Obama isn't American (and is a Communist Muslim).
Regarding P1, maybe the arguer hasn't encountered any evidence so we'll leave it alone. P2 however has problems. There have been reasonable searches for evidence, and that evidence was found. Perhaps, the arguer was unaware or didn't truly exert him/herself enough. The argument fails because P2 is not acceptable (i.e., false).
We can also typically find the argument from ignorance used in arguments against new (or relatively new) technologies in regards to safety or efficacy. For example:
We should ban GMOs because we don't know what long-term health effects are.
Dressed:
P1: I've found no evidence that shows that GMOs are safe for human consumption.
P2: Those qualified to do studies and evaluate evidence have found no compelling evidence to show that GMOs are safe for human consumption.
C: Therefore, we should assume GMOs are unsafe and ban them until we can determine they are safe.
If we were to criticize this argument we'd consider P2. In fact, there have been quite a few long term studies done by those qualified to assess safety. At this point we will have a debate over quality of evidence. Some on the anti-GMO side dispute the quality of the evidence (i.e., it was funded by company x, and therefore it is questionable). In a full analysis we'd consider this question in depth, but for our purposes here, we might legitimately challenge the claim that there is no available evidence purporting to demonstrate safety.
As an aside, notice that we can also use the argument from ignorance for the opposite conclusion: There's no compelling evidence to show that GMOs are unsafe for human consumption in the long-term, therefore, we should continue to make them available/ should not regulate them.
The "team" that wins this battle of arguments from ignorance will have much to do with our evaluation of P2: That there legitimately is or isn't quality evidence one way or the other.
Final Notes on Arguments from Ignorance
We can look at arguments from ignorance as probabilistic arguments. That is, given that there is little or no evidence for something, what is the likelihood that it still might exist? This is especially true for claims that something does exist based on an absence of evidence for its non-existence. However, as Carl Sagan famously said, "absence of evidence is not evidence of absence." In other words, just because we can't find evidence for something, doesn't mean that the thing or phenomena doesn't exist.
On the flip side, this line of argument can also be used to support improbable claims. Consider such an argument for the existence of unicorns or small teapots that circle the Sun: There's no positive evidence that unicorns don't exist or small tea pots don't circle the Sun, therefore we should assume they exist.
At this point we should return to the notion of probability: Given no positive evidence for these claims, what is the probability that they are true (versus the probability that they aren't)? It seems that, given an absence of evidence, the probability of there being unicorns is lower than the probability that they do not exist. Same goes for the teapot.
Typically, in such cases we say that the burden of proof falls on the person making the existential claim. That is, if you want to claim that something exists, the burden is upon you to provide evidence for it, otherwise, the reasonable position is the "null hypothesis." The null hypothesis just means that we assume no entity or phenomena exists unless there is positive evidence for its existence. In other words, if I want to assert that unicorns exist, using the argument from ignorance won't do. It's not enough for me to make the claim based on an absence of evidence. This is because, we'd expect some evidence to have turned up by now if there were unicorns (i.e., P2 of the implied argument would be weak).
This brings us to another Carl Sagan quote (paraphrasing Hume): "Extraordinary claims require extraordinary evidence." Or as Hume originally said: "A wise man proportions his beliefs to the evidence." Claiming that unicorns exist is an extraordinary claim and so we should demand evidence in proportion to the "extraordinariness" of the claim. This is why an ad ignorantium argument fails here; it doesn't offer any positive evidence for an extraordinary claim, only absence of evidence. We'll discuss this principle of proportionality more in the coming section. For now, just keep it in mind when evaluating existential arguments from ignorance.
Monday, November 25, 2013
The Scientific Method Lecture Notes
Introduction to the Scientific Method in the Context of Critical Thinking
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.
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