OK, here we are, a week for. Well done for staying with us so far this week, we're going to be looking at how to evaluate arguments,
how to tell whether an argument is a good one or a bad one. And we'll start with inductive arguments.
Right. Let's get started today.
Last week, we learnt how to analyse arguments, and what I meant by that was how to identify them and how to set them out.
Logic, books style. I gave you six steps to analyse an argument.
And these are the only steps and other thing that's become clear to me from emails and questions I've had this week is that a lot
of people are trying to evaluate an argument to say whether it's a good argument or a bad argument as they try and analyse it.
Well, don't, because you'll always be led astray if you try to do that, especially with a complicated argument like the one we looked at last week.
So follow just these steps. Don't do anything else to the argument.
Don't say I think the conclusions shouldn't have a knot in it here and take the not out
nots are really very important and they shouldn't be added in either if they're not there.
And so so just follow these steps. That's all you need to do.
I'm not suggesting it's easy. In fact, this is the hardest thing you'll ever do in logic.
Computers can't do this. Only we can do this. A computer can evaluate arguments very easily by appeal to just a very simple algorithm.
But what it can't do is translate an argument in English into a formal language.
Hopeless computers can't do that, or at least not unless a very, very, very simple.
So those are the those are the steps you must take to analyse arguments and don't try and evaluate them at the same time.
Okay. We did see that although we needed to paraphrase arguments in order to complete these steps.
In other words, we had to add in things that I mean, instead of it we put she or something like that or that wasn't a good example.
But instead of it, it was tried to tickle him more.
Do you remember? So we had to paraphrase arguments to complete this search.
By paraphrase, I just mean put what's there in different words, not change the meaning of anything.
And certainly don't add in any meanings or take away any meanings.
Paraphrase is just changing them the words so that the argument structure becomes clearer.
Do you see the difference? And again,
you'll probably need a bit of practise before that comes easily because it really is a temptation to evaluate the arguments and to change its meaning.
If you think it would be clearer if so-and-so said this rather than that,
but try to avoid that because what you're trying to do is identify the arguments as somebody else is making,
not the argument that you would make if you were in his position.
OK, this is the point about analysing arguments is in the hope that you might learn something and you
won't do that if you're imposing your own grid of understanding onto someone else's argument.
Okay. So paraphrase, but don't change the meaning. We also saw that it's necessary to bring to bear our understanding of the argument.
For example, do you remember the suppressed premises that we added last week?
I mean, we had quite a tussle with some of them, didn't we? Some of them turned out.
Some of the things that we thought might be suppressed premises turned out actually to be a matter of inconsistent terms or something like that.
So we have to bring to bear our understanding of the argument and what follows from that.
But don't read into the argument anything that isn't actually there.
If a suppressed premises there,
it's usually pretty clear that that's a suppressed premise of the argument is it's a premise that ought to be there but isn't.
So all you're doing is making it explicit, something that's already there implicitly.
OK. I think we're. OK. And I've just said it's extremely important in analysing an argument not to evaluate it.
First you identify it, then you evaluate it. OK.
Any questions about all that before I move on to today? Oh, no.
Okay, let's move on to today. What we're going to do today is to start learning how to evaluate arguments.
Now, today, I've got down to starting with validity and truth.
Looking at the distinction between them. But I've decided instead to start with induction and then go on to validity and truth next
week and then look at deductive arguments in the evaluation of them in the final week.
So we're going to deal with induction this week. Oh, OK.
Well, I've done it now. I was going to ask you to tell me what an inductive argument was, but no.
Well, OK. You knew this anyway, didn't you?
Yes. Good. OK. They give a fantastic inductive argument such as truth of their premises makes the truth of that conclusion more or less likely.
OK. And if you remember, we looked at two examples in the first place.
We looked at the sun's rising. The sun has risen every day in the history of the world.
Therefore, the sun will rise tomorrow. And every time you see Marianne, she's been wearing earrings.
So next time you see her, she'll be wearing earrings. I'm going to leave them off next week.
If I remember, all inductive arguments rely on the principle of the uniformity of nature, as Hume called it.
David Hume called it. And the only arguments for the principle of the uniformity of nature itself are themselves inductive.
So it looks as if any arguments you offer for induction is going to be circular based on induction itself.
And this is a this is a real problem. People would love to be able to justify the principle of the uniformity of nature.
So why we should believe that the future will be like the past.
But no one's conclusively succeeded there.
Does reams and reams and reams of books and papers written on this problem.
And there are lots of theories about it, but there's no theory on which everyone would converge yet.
Okay.
Different types of inductive argument, inductive generalisations, causal generalisations, arguments from analogy and or arguments from authority.
We're going to have a look at each of these separately and look at how to evaluate them.
So by how to evaluate how to tell whether they're good arguments or bad arguments, please remember, inductive arguments are not.
It's not a matter of either or with inductive arguments, they're either strong or weak.
Okay, so there is a great nation. It's a matter of degree as to how good an inductive argument is.
Okay, let's start with inductive generalisations.
And what do I mean by this is that the premise identifies the characteristic of a sample of the population of a population.
And the conclusion extrapolates that characteristic to the rest of the population.
And all inductive arguments are actually a form of this, of inductive generalisation.
So in learning how to evaluate inductive generalisations, you can apply everything you learn to other types of inductive generalisation.
But let's have a look at them generally. Okay, here are two examples. So, okay, looking first at this one.
What's the population that we're looking at here? So do you remember I said the premise points to a sample of a population.
And the conclusion extrapolates to the rest of the population.
So what do I mean by the population in this case? Voters.
Exactly. That's right. So we're saying here that 60 percent of the voters have been sampled and that 60 percent said they'd vote for Mr. Many promise.
And we're extrapolating from that to therefore, actually, there's a suppressed premise here, isn't there?
Or there's something we could add in here. Well, no, we'll move on to that in a minute.
We we we're sort of assuming, aren't we, that 60 percent of the of the population as a whole would be enough for him to win.
Do you see what I mean? Because that's implied by this, isn't it?
Rather than actually stated. OK. And then the other one we've got here, what's the sample?
Oh, sorry. What's the population here? One more calls to Beatty.
Calls to Beatty. So the premise says, whenever I've tried to ring Beatty, when either I've tried to make calls to Beatty, it's taken me hours.
And I'm extrapolating to that for that, too. Chase calls by me, actually, isn't it?
Rather than calls by calls generally. So I'm extrapolating from my past experience to my future experience correctly.
OK. So what I want you is to have a look at each of these arguments or you can choose just one of them.
If you if you want to do it more slowly and ask yourself to write down the questions
to which you would need answers in order to decide whether these are good arguments,
and then we'll go through them together. So have a look yourself.
And just think about these questions and think about what you would ask in order to satisfy yourself that these were good arguments.
Okay, anyone want to give me examples of the sort of questions you would ask?
First I'd ask. So it would be OK if if the electorate was just 10 people, why would that help you evaluate the argument?
Is it really the population, the number of the population you want or what else might it be?
You might want to know the size of the sample. Yep.
Yes. I thought you might, because if if you've got 10 people only where in the sample and yet there are a million people in the population,
then the sample just isn't big enough, is it? I've got another question,
because I know from experience that just because your vote says you actually vote and
so do you have to know what percentage of the people sampled are unlikely to vote?
I mean, implicit in your word, voters, because, no, it isn't implicit in the word voters.
You might want to. Yes.
I mean, one of the things you would certainly want to consider here is that the voters sampled said that they would vote for mr.
What is whatever his name is, but actually won't vote for him or may not vote at all.
Yes. I mean, either way, it wouldn't make much difference. So yes, I do think that's a yes because you can't see the heat.
Yes. That's a bit of background information that you would bring to bear on this particular argument.
It's something you know about voters which show that you really have to know a bit more about.
Well, you present presumed, I bet expect there's a number by which they they determine how many are likely to actually say.
I don't know. Yes, you would.
You'd certainly need to know whether they were telling the truth. Yes. Okay. Okay.
It's certainly the case that Mr many promises not likely to win if he's not going to stand, even if 60 percent of the votes.
So actually, that's quite a good counterexample, isn't it? A case where the premise would be true, but the conclusion would have to be false?
That is quite a good counter example to that.
If you've got a situation where the voters really did want to vote for whoever it was, but he wasn't going to stand.
Yeah, I like that one. Another one here. Good.
You'd want to know whether the sample is representative, wouldn't you?
Because if if the only people they asked were males,
then who knows what women are going to do or if they're all under 24 or if they're all black or if they're all.
Whatever. You need to know that the sample chosen is representative of the population as a whole.
Yes. Okay. So if you have something like the radio, there was a radio programme, wasn't there, that was taking votes for something or other.
And a lot of people. So, I mean, actually, what you want to do is you want to ask whether the premise here is true at all.
Yes, definitely. Yes. Yes. Because if six, maybe 60 percent of the voters said that they'll vote for Mr. Brown.
But then something dreadful happens. And. It's certainly not the case that if you sampled them again just before the election,
they would still say, good, you're coming up with all sorts of things. I haven't got myself here.
This is brilliant. Who did the sampling?
Yeah, that that would be a very good thing. And again, I mean, that's another example of is the premise true?
Because if the person is saying that 60 percent of the voters said that they would vote for him if they're all apparatchiks,
for Mr. many Promis who want to make him feel good before the election, you might question the premise itself, mightn't you?
OK. What about this one? Or is there anything that would be added to this one that we haven't already considered?
Gentlemen. When I think is perfectly good, because if I've been trying to bring Beattie at two o'clock in the morning, it might be perfect.
You know, yes, it may have taken hours, but were I to ring at 10 o'clock in the morning, it might be different.
I'm assuming they don't answer the phone at two o'clock in the morning. OK.
It's certainly reasonable to ask whether it's it's just me.
Yes. I mean, there might be something about my particular telephone number that whenever I ring Beattie,
there's something that says, don't answer this one or something like that.
But ask the conclusion is that when I ring Beattie, do you see what I mean?
Again, this this again, the way I've set this up, the population here is calls that I make to Beattie rather than calls that anyone makes Beattie.
Yes. I might have only made one or two. I again, that's structurally the same as when we said here.
How many people did we sample in the population and what percentage of that elderly population is that?
And you're suggesting exactly the same thing here. Quite properly. If I've only tried to ring once or twice, then.
Is that really a big enough sample? Good.
Again, you're questioning whether that's premises true. I mean, maybe I'm just very bad at calculating time.
Maybe I'm one of these people who's very keen to get somebody answer my phone call immediately.
And if it takes 30 seconds, then I get very irritated and thinks it's I think it's ours.
OK.
You would have to assume, wouldn't it, that it was the same part of Beatty again, because otherwise otherwise you get an equivocation, wouldn't you?
There's Beatty here. Wouldn't mean the same as Beatty here.
OK. An equivocation, by the way, is it is an argument in which you use the same word with two different meanings.
OK. So if you think of the word bank, it could mean financial institution.
It could mean an action of an aeroplane or it could mean the side of a river.
And if in an argument you used it in all three of those meanings,
you could imagine an argument that would look good, but as a matter of fact, wouldn't work at all.
And that's as a result of equivocation. You're equivocating on the word bank.
So if I were equivocating here on the word Beattie or the the letters Beattie, my conclusion might not follow from my premises.
OK. Very good. That really is good. I think it's very impressive.
You'll see as I go through the things that I'm going to list that you've said just about all of them.
OK, firstly, is that just about all of them? This one I think I've got that you haven't.
Is the premise true? Okay, we've got 60 percent of the sample said that they would vote for Mr.
Many promised. Well, can we really believe that? Might they be bad at record keeping?
So it actually wasn't 60 percent. It was only 50 percent.
Then, you know, if if you last year when he used those people, they were completely hopeless.
Might they be engaged in wishful thinking? Might they be bad, just bad at maths?
They can't work out percentages. Am I telling the truth?
Am I in the pay of one of Beattie's rivals? Am I prone to exaggeration?
Am I just very bad at estimating time? So lots of reasons why the premise itself might not be true.
And if you remember, whenever we're evaluating an argument, there are two things we've got to look at.
Can you remember what they are? Just two basic things we look at whenever we're evaluating an argument of any kind at all.
One is, does the conclusion follow from the premises? That's right, and the other is, is the premise are the premises true?
That's right. Is if if even one premise is false,
then then that doesn't guarantee the truth or the conclusion does it or doesn't even meet the truth of the conclusion more likely.
So first thing you look at when you look at any argument is, are the premises true?
Okay. How large is the sample? Again, you got this. How many of those who would vote in the election were sampled?
10 out of one million? Well, that doesn't look very good, does it?
A thousand out of one million. That looks better. How many is enough, though?
Do you think? And that's a really difficult question, isn't it?
How many is enough? I'm just specifying here that one million is the population.
And then we're saying, OK, how many of those would count is enough?
And I'm saying there actually isn't any answer to that. We can certainly answer that.
Ten is probably not enough. And we might be able to say that.
Nine hundred and ninety nine. Well, thousands or tens. I don't even know how much a million is a thousand thousand, isn't it?
Okay. Nine hundred thousand would be enough. Okay.
But in between those two numbers, what counts is enough statistics, so.
Well, that's coming later. That's coming when we look at the representativeness of the sample at the moment.
The only thing we're talking about is the size of the sample. If I say all swans are white and you say, well, what's your reason for saying that?
And I say, well, I saw Swan just now and it was white. And you say, what?
Just one. I see. And you can be more or less inductively bold.
And actually, if we were to look at people in this room,
if we were to do a headcount of people in this room, we'd find that some of us are very large.
I shouldn't say ask because I'm I'm not inductively bold.
But some of us would be prepared to extrapolate from a very small number and others of us would be very sceptical about it.
Strapped rating, even from quite a large number. So actually the question, how many is enough?
The answer would be it depends on who you are on and on how inductively bold you are.
Well, statistics statisticians have to come up with something that they would count has enough evidence, right?
Yep. And the larger sample of the competence, right?
They can be more competent to represent. When you say the larger the sample.
Do you mean that? It's certainly true that if the thousands have been samples, that's much more confidence boosting than 10.
Yes, that is. That's what you mean. Yes. Yes. Okay.
Are they saying that as a result that. 80 percent in the election will vote one way plus or minus two percent.
All they're saying. Fifty five percent plus or minus 10 percent.
And they leave the range of their prediction depends upon the size of the sample.
Yes. No, I think, you know, I'm I'm getting out of my depth here.
I don't understand what you're saying, I'm afraid, and it's a good example from history, such as an American election in mind.
So, yes, we're coming to that. That's representativeness. Yes. Yes.
You know, it's it's not that sweet song. So, you know, let's let's leave representativeness aside at the moment, I'm just at the moment.
I'm just talking about size. All we need to look at is how many of those in the sample, how many in the population,
how many in the sample do we think that we've got enough who've been sampled in order to make us more confident about the extrapolation.
If I've only rung BBT once, then my claim that the next time I'm going to ring is is really pretty low, isn't it?
It's a very weak argument. Whereas if I've rung Beattie 50 times and not got through.
Then that's more reason to think. So if we think remember that inductive arguments make the premises, make the conclusion more or less likely.
Well, if my premises. I've rung Beattie once in the past and it took them hours to answer then, so it'll take Miles to answer again.
My argument is much less strong than if I say I've rung Beattie 50 odd times in the past and it's taken them hours to answer.
Therefore, it'll take Miles to answer next time. See what I mean? And again, here, if I say 10 out of what, 60 percent of 10.
In other words, six voters out of a million said that they'd be voting for Mr many promise.
Therefore, Mr many promise will win the election.
That's a less good argument, a weaker argument than if I say 60 percent of a thousand voters say that they'll vote for Mr many promise.
Therefore, Mr many promise will 60 per cent of people will vote for him in the election and he'll win.
See what I mean? We haven't actually looked at representativeness yet.
We will about to do so. I know you're all dying to get onto on representativeness, so let's do so.
Here we go. OK. The second thing that we ask is how representative is the sample, what you should do instantly.
I'm giving you again, you might say it was another algorithm, another just list of steps that you might do.
Again, try and keep them separate in your mind, because if you tick off each one, OK?
You've asked yourself how many there are in the sample and how many there are in the population.
I made a judgement about whether there is enough in the sample to be able to to extrapolate.
Second question you ask is whether the sample is representative. See what I mean to Descartes.
Very famous philosopher, brilliant philosopher.
I had a list of rules of thinking and one of the things he said was that you should take any problem you have and break it up into its parts
and then deal with each part separately and then make sure that looking at each of the parts you can put together as a solution to the whole.
And what I'm suggesting is you ask each of these questions separately so that you make sure that you ask all of them.
I mean, it just makes your thought clearer. Again, as with first, you identify the arguments and analyse it, then you evaluate it.
OK. And you don't try and do both at once. OK.
So here again, you got all these were the voters sampled all female?
Well, I mean, there are a lot of medical experiments or medical surveys that look only at men and then extrapolate the results to women.
I don't know if you've seen recently they've decided that for women,
the symptoms of a heart attack are quite different from the heart attack symptoms of a man.
And therefore,
all the extrapolation that they've done in the past from male experience of heart attacks to female experience of heart attacks has been faulty.
There was quite a big thing about that a couple of weeks ago. And are they all over 40?
Are they all white? Are they all middle class? Are they all known to the person conducting the survey,
the famous example that you were mentioning a minute ago and in fact, that you've just mentioned as well.
In an election between who, Roosevelt and London?
That's right. They thought that 60 percent of the population was going to vote.
That's what their sample of said. But how did they find the sample?
They looked in the telephone book. How many people had telephones then?
Actually, very few. So although there was 60 per cent of the sample said that they would vote for Roosevelt.
Actually, the sample was horrendously unrepresentative because it was middle class people with fair amount of money who had telephones.
And therefore, it didn't represent the population as a whole. OK.
And anyway, the same thing here again, we came as have I only rung Beattie on the Sunday after 10 pm when I'm in a hurry, et cetera, et cetera.
Okay, so firstly, it's the premise. True. Secondly, what was it?
How large is the sample as a percentage of the population? Thirdly, how representative is the sample?
Three questions to ask there. Here's another one.
Here's a one you haven't thought often. Perfectly reasonable that you shouldn't if you were asked a hair or two hands of cards.
Which one is most likely to come up? And who thinks this one is most likely to come up?
No. OK. Who thinks this one is most likely to come up? No, you're all very clever, aren't you?
You're absolutely right. And they're actually equally likely to come up because, of course, cards are just at random.
They're not. But actually, if if you if you ask the students at the university where this experiment was done,
which hands is likely to come up, they come out overwhelmingly against this one.
And for this one, this is much more likely to come up than this one. Now you can see why they think this can't you can do this.
This one. Well, yes. This is the one they'd love to have come up.
And this is the one that they have come up. They think all the time sort of thing.
But, of course, actually, it doesn't quite work like that because they're using an informal heuristic to say, in my experience, this never comes up.
And this always comes up. And actually, you just can't use that here, can you?
Because what comes up is something like that. But certainly not that.
It just means a way of making a decision. OK.
Rule of thumb, if you like. A way of making a decision. Thank you for asking.
I should have explained it before. OK. So.
So if an inductive generalisation is based on on an informal claim like this, in my experience, hands like this never come up.
Therefore this one is is much less likely than that one.
Then you should be very wary of the generalisation.
And here's another one. And I expect you're all going to be clever enough to get this too.
OK. Four pages of a novel. How many words would you expect to find ending in English and in four pages of a novel?
How many words would you expect to find that includes the letter N? Would you expect that to be larger than that?
Or vice versa. So you'd expect more of being words.
No more of the N words. Put up your hands if you think there are more N words.
OK. Put up your hand if you think more in words. OK, that's interesting.
This time you have fallen for the trick because, of course, there are going to be words in words.
That's right. They're always going to have. Oh, okay. I'm sorry. So you're absolutely right.
There are going to be many more N words than there are in words, because they're all they're going to be at least as many words as there are in words.
Yes. Okay. Sorry, you did get that. What happens again when you ask these students,
the psychology students at the university where this experiment was done is they expect
many more of these because they can think of many more in words than they can of N words,
and therefore they they inductively generalise again. Well, I can think of many more of those.
Therefore, there probably are more of those. Again, bad arguments.
If I ask you how many footballers or something from a particular team score, well, you'll be able to think why it is.
That's a very bad example. Anything that you think you know a bit about,
you're probably tempted to rely on your own experience to make an inductive
generalisation that can work if you really do know what you're talking about.
But it doesn't work if you're just using that way of doing it on on another context where actually your knowledge is not so.
Secure. OK. OK, so five steps there, I think it was when you are evaluating any inductive generalisation you're looking for.
Firstly, is the premise true? Secondly, does the sample size of the population.
Is it large enough compared to the population as a whole?
Thirdly, is the sample representative or is there a bias in it due to whatever all sorts of reasons for different biases?
And finally,
is it based on on an informal heuristic that actually an informal rule of thumb that actually just won't stand up to proper scrutiny here?
OK. And as I said, all inductive general, all inductive arguments are based on inductive generalisations.
And so that little way of testing things can be used for all of them.
Let's look at causal generalisations. Okay. A causal generalisation is a type of inductive generalisation.
The previous identifies a correlation between two types of event and the conclusion states that events the first type cause events of the second type.
So the idea is that if you see A and B, A and B and B, A and B, A's and B's are always correlated.
You extrapolate to the claim that A's and B's will always be correlated.
And you imply that the reason for this is that there's a causal relation between them.
So where there's correlation, there's cause that sort of causal generalisation is.
So let's have a look at a couple. Okay. Married men live longer than single men.
Therefore, being married causes you to live longer. I apologise for this one.
When error is allowed into a wound magots form. Therefore, maggots in wounds are caused by air being allowed into the womb wound.
This is. Sorry. Okay.
I'll tell you what. Let's let's do it openly. What do we need to know to know whether this these arguments are good arguments.
Okay, let's have a look. Again, we ask, is the premise true?
Who says, man, married men live longer? Married men. A woman who wants to get married.
Fred, whose parents split up when he was five. I mean, who who's saying this?
Where are we actually getting this information from? Who says magots form when I get since the womb?
Just as you said at the back there. Was it a newly qualified nurse who Soos observed this once?
Was it an elderly doctor who is seen as a lot, but only in his own experience and in his own study, perhaps?
Or was it a scientific study of one that you would expect to be to have looked more carefully?
Causation is actually I mean, to give you a little bit of background on this.
David Hume, the person I've mentioned already in connexion with the principle of the uniformity of nature, believes that actually causation.
We cannot determine causation. If we find A causes B and we try and find out why A causes B, what what is this causal relation?
What is it that relates the two things that would cause and effect?
We'll just find another correlation. CND. Okay.
So why do we think C and D are correlated? We look further and we look down and we see yet another correlation.
So all we ever see is correlation. We never actually see the causal relation itself.
We can never get to the causal relations itself.
And he actually thought arguably this is a very popular theory of Hume, although lots of people deny it.
These days that he actually thought causation didn't exist at all.
That causes that are beliefs about causation or just the habit of mind.
So we see a correlation would be a correlate, it would be A correlated with B,
and we start to say that A causes B, and all we mean by that is that A is correlated.
B, there's just a constant conjunction between A and B, there's nothing that makes A cause.
B. I have to say that there is another theory of Humes that that he says that A causes B, where has it not been the case that,
A, it would not have been the case that, B, had it not been the case, that it would not have been the case that B.
And that suggests that there is a power of some kind, isn't there?
That makes A cause B, but but we do not have a C that power, do we.
We don't. We just see the cause and the effect and the correlation between them.
And so causation is is a really interesting philosophical issue.
The question what's causation is, is endlessly interesting.
I think it's endlessly interesting, but it remains to be the case that our evidence for causation is always a correlation,
but a correlation simply isn't sufficient as evidence for court of causation, is it because it could be evidence for identity, for example.
So that night. Well, the evening star goes down.
Morning star rises and so on. So forth. Do they cause each other to do it?
No, actually, they're the same thing. That's why they're correlated.
That's why the pattern is uniform. Do husbands cause wives?
But they're correlated.
Well, what we're saying is that correlation isn't sufficient for a causation, but it's the only evidence wherever likely to have.
But when you when you see a causal generalisation, it will be based on correlations.
But what we're alerting you to here is that a correlation isn't sufficient for a causation.
You need to ask lots of other questions. So is the premise true?
How strong is the correlation? How many married men were observed?
I mean, this is, again, exactly the same as how many are in the sample from the last question.
How long were they observed? Were unmarried men observed? How many cases of maggots forming were observed?
And because when John Stuart Mill, famous philosopher, English philosopher,
came up with what he called the method of agreement and the method of difference for scientific experiments.
What if you're trying to work out what causes what you need to see?
Firstly, that they do correlate that, that the cause correlates with the effect.
Next thing you need to do is to try and bring about the cause without the effect.
Because if you're saying that is cause B because all A's are always correlated with B,
then what you do is you try and bring about an A without A, B, B is if you can do that, you've disproved your claim about causation.
See what I mean? And that shows us that we tend to think that a cause is sufficient for its effect, that if A causes B,
the occurrence of an A must be followed by the occurrence of a B because A is sufficient for B.
So that that's the method of of sameness is and the method of differences, which tells you whether something is a cause or not.
And also, you want to ask, does the causal relation make sense or could it be accidental?
Let's say that we discovered that in the whole history of the universe, every time a match has been struck, a pineapple has fallen.
OK. We have a correlation and we've done our very best to try and make sure that we've struck matches
without a pineapple falling and keep on doing it so we can't break the correlation in any way.
Do we think that match is striking cause pineapple's to fall?
Well, some people are quite inductively bold here, they think, yes, if you've got correlation as strong as that, it must be causal.
Apparently, there's also a correlation between the length of skirts and the Dow Jones index.
As one goes up, the other goes up. And this one goes down. The other goes down.
It might be the other way round. But anyway, there's a correlation here.
Do we think that the length of skirts causes the rise and fall of the Dow Jones index or vice versa?
That's more likely. You can sort of see something that makes sense.
Can't you, in that? Because you may be when the down to Jones index is really high.
People are really excited and pleased and therefore they risk taking risk taking.
So they put on their mini skirt. Okay.
It does. Okay. So the claim that being married makes you live longer if you're a man.
Why? Why would being married cause men to live longer?
I think this is where your claim about all we, including civil relationships, is quite interesting.
Why would being married cause men to live longer? Certainly, I think it causes women die earlier.
Just a warning to women and through. Okay, they're happier.
The stress reduces another explanation. Well, that's a first.
He. It might also be because women tend to look after diets and things like that.
More men are cooks for more often than women are perhaps.
And when women do the cooking, they concerned about nutrition and da da da da.
So when a married man eats, he tends to eat more healthily than mean that we can think of reasons for why that would be the case, can't we?
So it's not a complete mystery. What about this one?
Why would Air getting into a wound cause maggots to form so.
So, I mean, the experiment we've done here with some nurse has seen that when a wound was covered up by accidents or something like that,
maggots didn't form. And she thinks, well, you know, could it be so?
She covers up a few and she leaves a few open and she sees that the one she's covered up don't get maggots,
whereas the ones that left open do get maggots. So she's formed a hypothesis.
Could it be that getting into the wound causes maggots? But why would that be the case?
Perhaps because there's something carried in the air that causes maggots to form.
And actually, we know now that that is the case. So, okay.
So what does the causal relation make sense? Incidentally, if it doesn't make sense, does that mean it's not causal?
No, it doesn't touch you, does it? Cause you can imagine that there may be something that is a complete mystery for us for a while.
And I wish I could think of an example, but which turns out to be true and turns out to have an explanation.
But even so, if you can't if if it just if these things seem to be just totally disparate,
that would be a mark against this argument being a good one.
And we also might and we've done this a bit. Okay. What's cause it is what could it be that being long lived causes marriage?
So it might be that having genes for longevity caused men to get married.
So you said socio economic factors. But I'm suggesting could be genetic factors.
So there's one set of genes such that if a man has them, he's both more likely to get married and he's more likely to live longer.
So there's one common cause for the two things, rather that one thing causes the other.
And that I couldn't think of anything could Magots forming cause to get into the womb?
No, I couldn't think about that, so. Okay.
Right. That's so that's looking at causal generalisations. And you'll see that many of the questions that you would ask about causal generate
generalisations are also questions you've already asked about inductive generalisations.
That's not surprising because causal generalisations are a type of inductive generalisation.
And all the ones that you're asking separately, the ones that say, you know, why should we think that a correlation has a causal relation under it?
Say that. So just moving on quickly to an analogy here, another type of inductive generalisation,
it takes just one sample of something and then extrapolates from a character of that example to the character of something similar to that thing.
And there's a famous argument from analogy. The universe is like a pocket watch.
Pocket watches have designers. Therefore, the universe must have a designer.
I think we're probably all familiar with that argument. Okay.
How would we go about questioning this argument? Yes.
OK. And what aspects are we picking out here and saying is similar to the two cases?
So why is the universe like a pocketwatch? I mean, using this famous example, what did the person believe?
Was it complicated? Lover. That's right.
It was very. Who was it? It's gone completely, palely.
That's right. Thank you. I'm sorry. I have got a head full of cotton wool. It's very strange.
Yeah. Paly believed that the universe the pocket watches is moves regularly.
It's very complex. It's. It must be very difficult to put together.
And he believes that the universe is also very regular, very complex.
It must have been difficult to put together. Therefore, if one has a designer, the other has a designer.
What else might you ask? Okay. There are many, though.
There is a similarity, we might say, between pocket watches and the universe.
But there are many, many dissimilarities. Why?
Why should we consider that this similarity is more important than all these differences?
Yep. Okay.
But wouldn't you say that if the universe is like a pocket watch in this particular thing and the explanation pocket is what she's having a designer,
it is this particular thing. In other words, it's being very complex. So if we agree that everything that's complex and regular must have a designer.
OK, but we are saying that the universe is like a pocket watch in being very complex and regular pocket watches have a designer.
Oh, I see. Okay, so you're absolutely right.
I'm sorry. No, you are right. I was I was changing that second premise to everything that's complicated has a designer.
And that's not what it says, is it? And so I've rightly been pulled up on that.
Okay. It isn't what it says. I suppose that's why we think that this is going to work at all, though, isn't it?
In order to give an argument, we do have to say a lot of things in support of the various premises and in
support of our belief that the conclusion follows from the premise and so on.
So you wouldn't expect almost anything said to be an argument.
And actually, as you learn. Yes.
No, I'm not surprised. I'm mean, I suppose what I'm doing is I'm defending newspapers because actually you need to
read a whole or a whole article in order to see what the claim being made is.
And then you need to go back and identify what the reasons are being given for the claim.
Okay. All the two things similar in there and the respective is there is the respect in which they're similar relevant to the argument being made.
And also, can we find a decent allergy,
which is the thing you mentioned is are there differences between them and do the differences pertain to this argument?
But the thing to remember about arguments from analogy is that they are extrapolating from just one example.
Therefore, the one example and the extrapolation have to be really pretty strong before you should go along with them.
So arguments from are actually arguments from analogy are much more common and probably for the reason you're saying,
because they often take us along with them emotionally.
Let's finally look at arguments, room authority, which take one person or a group of persons who are or are assumed to be right about some things.
And they extrapolate to the claim that they're right about other things.
So human rights monitoring organisations are experts on whether human rights have been violated.
They say that some prisoners are mistreated tonight in Mexico. Therefore, some prisoners are mistreated in Mexico.
What do we need to ask about this? Where do they get their information from?
Is it just that they've become hackneyed and cynical and they think that everyone mistreats everyone?
Or do they actually have reasons for saying what they have? Yep.
I mean, all is needed for this argument is that some prisoners are mistreated. Not that they're mistreated by anyone in particular.
I think. OK. You might say here we've solved the first previous here is they may be experts on whether human rights are being violated,
but are they experts on whether somebody is been mistreated or are they perhaps
seeing trivial forms of mistreatment as violations of human rights or something?
Is that what you mean? Yep. Okay. Okay. Well, let's have a look at the.
Okay. Who exactly is the source of information?
I mean, it was saying that it was implying at least that all the human rights organisations were saying it, but it might just say one.
And again, there you would make you'd want to make a judgement about whether the source of information really is an expert,
whether they they're qualified in the appropriate area, because it's very easy.
Again, going back to how inductively bold you are, if you have a tendency to think this person is an expert in one area,
you may well inductively generalise to his or her being an expert in another area.
So your tutor, for example, whom you think is you know, if Marijan says P, then P, which is of course a very good argument.
But if what she's talking about is politics or mathematics or something like that, that is complete nonsense, isn't it?
Okay. So so not only that, you need to know who they are.
You need to know whether they're qualified in the right area. You need to know whether they're impartial in this in respect to this particular claim.
So Amnesty International, let's say, are impartial.
They go out and they get the evidence and they're very careful not to be biased.
I don't know whether that's true and certainly. But let's say it could be.
But then there might be another human rights organisation that's not careful to make sure that its information isn't biased.
So you'd need to make a distinction between the fact that amnesty is is a reputable organisation and this other one isn't.
Well, I mean, you you get that quite often, I mean. I mean, if if you want to belittle the results that come out of a particular survey,
one way of doing it is to say that the people who are who are putting forward this survey are biased.
So I've been working on. Look, is GM food.
And actually, it's very, very difficult to to get a source that hasn't been funded by a pharmaceutical company or by a company
that isn't that that's the sort of the soil association or somebody that's very anti GM food.
So finding something that really is an impartial source is really very difficult.
And it's very, very important to to try and find one if you're really going to evaluate these arguments.
Finally, the point you made a minute ago. It's very rarely the case that you have one expert in an area and it's very
rarely the case also that all the experts in an area will agree on on something.
And if you have different experts making different claims, you need to make a judgement as to where you think.
Which of them do you think is is correct and what you cannot rely on?
There is an argument from authority, can you? Because they're both authorities.
So if you were an undergraduate writing an essay or indeed if you were you writing an essay on philosophy for me,
I would have given you lots of reading. You would have done the reading. And I would have expected you to come away and to think, okay, well.
So-and-so says this and Thingamabobs says that, and he says P and he says not P.
Well, which of them is the case? Well, now you need to look at what the arguments are that so-and-so gives, what the arguments are,
that thingummy Bob Gibbs and work out which ones you think are the best ones and why.
OK. So there's no substitute for thinking for yourself. An appeal to an argument for authority is okay for for various things.
I mean, we have to rely on authorities for all sorts of things.
But if you were trying to write a philosophy essay saying Marijan says P, therefore P will not do.
And that's true of every philosopher you ever come across. Because there are very, very few things in philosophy that aren't questioned.
Okay. That's where I was going today. Next week, we'll look at validity and truth.
And then we'll turn to the evaluation of deductive arguments.