1 00:00:00,330 --> 00:00:07,590 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, 2 00:00:07,590 --> 00:00:14,470 how to tell whether an argument is a good one or a bad one. And we'll start with inductive arguments. 3 00:00:14,470 --> 00:00:17,260 Right. Let's get started today. 4 00:00:17,260 --> 00:00:24,010 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. 5 00:00:24,010 --> 00:00:30,250 Logic, books style. I gave you six steps to analyse an argument. 6 00:00:30,250 --> 00:00:37,540 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 7 00:00:37,540 --> 00:00:45,490 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. 8 00:00:45,490 --> 00:00:55,060 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. 9 00:00:55,060 --> 00:01:01,240 So follow just these steps. Don't do anything else to the argument. 10 00:01:01,240 --> 00:01:07,090 Don't say I think the conclusions shouldn't have a knot in it here and take the not out 11 00:01:07,090 --> 00:01:12,350 nots are really very important and they shouldn't be added in either if they're not there. 12 00:01:12,350 --> 00:01:16,720 And so so just follow these steps. That's all you need to do. 13 00:01:16,720 --> 00:01:22,090 I'm not suggesting it's easy. In fact, this is the hardest thing you'll ever do in logic. 14 00:01:22,090 --> 00:01:31,390 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. 15 00:01:31,390 --> 00:01:37,750 But what it can't do is translate an argument in English into a formal language. 16 00:01:37,750 --> 00:01:43,330 Hopeless computers can't do that, or at least not unless a very, very, very simple. 17 00:01:43,330 --> 00:01:52,980 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. 18 00:01:52,980 --> 00:01:58,840 Okay. We did see that although we needed to paraphrase arguments in order to complete these steps. 19 00:01:58,840 --> 00:02:06,100 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. 20 00:02:06,100 --> 00:02:09,910 But instead of it, it was tried to tickle him more. 21 00:02:09,910 --> 00:02:14,440 Do you remember? So we had to paraphrase arguments to complete this search. 22 00:02:14,440 --> 00:02:24,490 By paraphrase, I just mean put what's there in different words, not change the meaning of anything. 23 00:02:24,490 --> 00:02:28,510 And certainly don't add in any meanings or take away any meanings. 24 00:02:28,510 --> 00:02:34,850 Paraphrase is just changing them the words so that the argument structure becomes clearer. 25 00:02:34,850 --> 00:02:36,760 Do you see the difference? And again, 26 00:02:36,760 --> 00:02:46,240 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. 27 00:02:46,240 --> 00:02:51,100 If you think it would be clearer if so-and-so said this rather than that, 28 00:02:51,100 --> 00:02:57,760 but try to avoid that because what you're trying to do is identify the arguments as somebody else is making, 29 00:02:57,760 --> 00:03:02,310 not the argument that you would make if you were in his position. 30 00:03:02,310 --> 00:03:08,440 OK, this is the point about analysing arguments is in the hope that you might learn something and you 31 00:03:08,440 --> 00:03:15,420 won't do that if you're imposing your own grid of understanding onto someone else's argument. 32 00:03:15,420 --> 00:03:24,970 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. 33 00:03:24,970 --> 00:03:29,880 For example, do you remember the suppressed premises that we added last week? 34 00:03:29,880 --> 00:03:33,760 I mean, we had quite a tussle with some of them, didn't we? Some of them turned out. 35 00:03:33,760 --> 00:03:41,500 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. 36 00:03:41,500 --> 00:03:47,290 So we have to bring to bear our understanding of the argument and what follows from that. 37 00:03:47,290 --> 00:03:51,670 But don't read into the argument anything that isn't actually there. 38 00:03:51,670 --> 00:03:53,620 If a suppressed premises there, 39 00:03:53,620 --> 00:04:01,870 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. 40 00:04:01,870 --> 00:04:08,290 So all you're doing is making it explicit, something that's already there implicitly. 41 00:04:08,290 --> 00:04:16,320 OK. I think we're. OK. And I've just said it's extremely important in analysing an argument not to evaluate it. 42 00:04:16,320 --> 00:04:19,800 First you identify it, then you evaluate it. OK. 43 00:04:19,800 --> 00:04:30,820 Any questions about all that before I move on to today? Oh, no. 44 00:04:30,820 --> 00:04:37,240 Okay, let's move on to today. What we're going to do today is to start learning how to evaluate arguments. 45 00:04:37,240 --> 00:04:41,260 Now, today, I've got down to starting with validity and truth. 46 00:04:41,260 --> 00:04:48,460 Looking at the distinction between them. But I've decided instead to start with induction and then go on to validity and truth next 47 00:04:48,460 --> 00:04:53,650 week and then look at deductive arguments in the evaluation of them in the final week. 48 00:04:53,650 --> 00:04:59,650 So we're going to deal with induction this week. Oh, OK. 49 00:04:59,650 --> 00:05:04,450 Well, I've done it now. I was going to ask you to tell me what an inductive argument was, but no. 50 00:05:04,450 --> 00:05:08,770 Well, OK. You knew this anyway, didn't you? 51 00:05:08,770 --> 00:05:18,130 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. 52 00:05:18,130 --> 00:05:21,670 OK. And if you remember, we looked at two examples in the first place. 53 00:05:21,670 --> 00:05:27,160 We looked at the sun's rising. The sun has risen every day in the history of the world. 54 00:05:27,160 --> 00:05:31,930 Therefore, the sun will rise tomorrow. And every time you see Marianne, she's been wearing earrings. 55 00:05:31,930 --> 00:05:35,890 So next time you see her, she'll be wearing earrings. I'm going to leave them off next week. 56 00:05:35,890 --> 00:05:43,420 If I remember, all inductive arguments rely on the principle of the uniformity of nature, as Hume called it. 57 00:05:43,420 --> 00:05:52,420 David Hume called it. And the only arguments for the principle of the uniformity of nature itself are themselves inductive. 58 00:05:52,420 --> 00:06:01,000 So it looks as if any arguments you offer for induction is going to be circular based on induction itself. 59 00:06:01,000 --> 00:06:07,420 And this is a this is a real problem. People would love to be able to justify the principle of the uniformity of nature. 60 00:06:07,420 --> 00:06:13,420 So why we should believe that the future will be like the past. 61 00:06:13,420 --> 00:06:16,780 But no one's conclusively succeeded there. 62 00:06:16,780 --> 00:06:23,350 Does reams and reams and reams of books and papers written on this problem. 63 00:06:23,350 --> 00:06:32,360 And there are lots of theories about it, but there's no theory on which everyone would converge yet. 64 00:06:32,360 --> 00:06:32,710 Okay. 65 00:06:32,710 --> 00:06:41,410 Different types of inductive argument, inductive generalisations, causal generalisations, arguments from analogy and or arguments from authority. 66 00:06:41,410 --> 00:06:46,240 We're going to have a look at each of these separately and look at how to evaluate them. 67 00:06:46,240 --> 00:06:53,170 So by how to evaluate how to tell whether they're good arguments or bad arguments, please remember, inductive arguments are not. 68 00:06:53,170 --> 00:06:58,870 It's not a matter of either or with inductive arguments, they're either strong or weak. 69 00:06:58,870 --> 00:07:05,770 Okay, so there is a great nation. It's a matter of degree as to how good an inductive argument is. 70 00:07:05,770 --> 00:07:09,780 Okay, let's start with inductive generalisations. 71 00:07:09,780 --> 00:07:17,140 And what do I mean by this is that the premise identifies the characteristic of a sample of the population of a population. 72 00:07:17,140 --> 00:07:22,270 And the conclusion extrapolates that characteristic to the rest of the population. 73 00:07:22,270 --> 00:07:28,840 And all inductive arguments are actually a form of this, of inductive generalisation. 74 00:07:28,840 --> 00:07:37,570 So in learning how to evaluate inductive generalisations, you can apply everything you learn to other types of inductive generalisation. 75 00:07:37,570 --> 00:07:46,780 But let's have a look at them generally. Okay, here are two examples. So, okay, looking first at this one. 76 00:07:46,780 --> 00:07:56,050 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. 77 00:07:56,050 --> 00:08:00,550 And the conclusion extrapolates to the rest of the population. 78 00:08:00,550 --> 00:08:06,910 So what do I mean by the population in this case? Voters. 79 00:08:06,910 --> 00:08:16,450 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. 80 00:08:16,450 --> 00:08:21,910 And we're extrapolating from that to therefore, actually, there's a suppressed premise here, isn't there? 81 00:08:21,910 --> 00:08:34,510 Or there's something we could add in here. Well, no, we'll move on to that in a minute. 82 00:08:34,510 --> 00:08:43,460 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. 83 00:08:43,460 --> 00:08:49,320 Do you see what I mean? Because that's implied by this, isn't it? 84 00:08:49,320 --> 00:08:55,950 Rather than actually stated. OK. And then the other one we've got here, what's the sample? 85 00:08:55,950 --> 00:09:11,320 Oh, sorry. What's the population here? One more calls to Beatty. 86 00:09:11,320 --> 00:09:19,930 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. 87 00:09:19,930 --> 00:09:24,910 And I'm extrapolating to that for that, too. Chase calls by me, actually, isn't it? 88 00:09:24,910 --> 00:09:37,450 Rather than calls by calls generally. So I'm extrapolating from my past experience to my future experience correctly. 89 00:09:37,450 --> 00:09:43,510 OK. So what I want you is to have a look at each of these arguments or you can choose just one of them. 90 00:09:43,510 --> 00:09:50,650 If you if you want to do it more slowly and ask yourself to write down the questions 91 00:09:50,650 --> 00:09:55,960 to which you would need answers in order to decide whether these are good arguments, 92 00:09:55,960 --> 00:09:59,590 and then we'll go through them together. So have a look yourself. 93 00:09:59,590 --> 00:10:11,620 And just think about these questions and think about what you would ask in order to satisfy yourself that these were good arguments. 94 00:10:11,620 --> 00:10:17,250 Okay, anyone want to give me examples of the sort of questions you would ask? 95 00:10:17,250 --> 00:10:31,160 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? 96 00:10:31,160 --> 00:10:40,920 Is it really the population, the number of the population you want or what else might it be? 97 00:10:40,920 --> 00:10:44,640 You might want to know the size of the sample. Yep. 98 00:10:44,640 --> 00:10:55,860 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, 99 00:10:55,860 --> 00:11:00,410 then the sample just isn't big enough, is it? I've got another question, 100 00:11:00,410 --> 00:11:07,100 because I know from experience that just because your vote says you actually vote and 101 00:11:07,100 --> 00:11:14,320 so do you have to know what percentage of the people sampled are unlikely to vote? 102 00:11:14,320 --> 00:11:24,340 I mean, implicit in your word, voters, because, no, it isn't implicit in the word voters. 103 00:11:24,340 --> 00:11:26,590 You might want to. Yes. 104 00:11:26,590 --> 00:11:33,640 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. 105 00:11:33,640 --> 00:11:40,360 What is whatever his name is, but actually won't vote for him or may not vote at all. 106 00:11:40,360 --> 00:11:49,940 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. 107 00:11:49,940 --> 00:11:55,560 Yes. That's a bit of background information that you would bring to bear on this particular argument. 108 00:11:55,560 --> 00:12:01,210 It's something you know about voters which show that you really have to know a bit more about. 109 00:12:01,210 --> 00:12:09,290 Well, you present presumed, I bet expect there's a number by which they they determine how many are likely to actually say. 110 00:12:09,290 --> 00:12:14,870 I don't know. Yes, you would. 111 00:12:14,870 --> 00:12:19,010 You'd certainly need to know whether they were telling the truth. Yes. Okay. Okay. 112 00:12:19,010 --> 00:12:25,880 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. 113 00:12:25,880 --> 00:12:35,360 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? 114 00:12:35,360 --> 00:12:38,180 That is quite a good counter example to that. 115 00:12:38,180 --> 00:12:44,640 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. 116 00:12:44,640 --> 00:12:49,190 Yeah, I like that one. Another one here. Good. 117 00:12:49,190 --> 00:12:52,840 You'd want to know whether the sample is representative, wouldn't you? 118 00:12:52,840 --> 00:12:56,990 Because if if the only people they asked were males, 119 00:12:56,990 --> 00:13:04,880 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. 120 00:13:04,880 --> 00:13:11,420 Whatever. You need to know that the sample chosen is representative of the population as a whole. 121 00:13:11,420 --> 00:13:20,480 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. 122 00:13:20,480 --> 00:13:29,840 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. 123 00:13:29,840 --> 00:13:36,980 Yes, definitely. Yes. Yes. Because if six, maybe 60 percent of the voters said that they'll vote for Mr. Brown. 124 00:13:36,980 --> 00:13:45,390 But then something dreadful happens. And. It's certainly not the case that if you sampled them again just before the election, 125 00:13:45,390 --> 00:13:49,500 they would still say, good, you're coming up with all sorts of things. I haven't got myself here. 126 00:13:49,500 --> 00:13:54,720 This is brilliant. Who did the sampling? 127 00:13:54,720 --> 00:14:02,610 Yeah, that that would be a very good thing. And again, I mean, that's another example of is the premise true? 128 00:14:02,610 --> 00:14:10,500 Because if the person is saying that 60 percent of the voters said that they would vote for him if they're all apparatchiks, 129 00:14:10,500 --> 00:14:18,600 for Mr. many Promis who want to make him feel good before the election, you might question the premise itself, mightn't you? 130 00:14:18,600 --> 00:14:25,170 OK. What about this one? Or is there anything that would be added to this one that we haven't already considered? 131 00:14:25,170 --> 00:14:34,050 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. 132 00:14:34,050 --> 00:14:41,190 You know, yes, it may have taken hours, but were I to ring at 10 o'clock in the morning, it might be different. 133 00:14:41,190 --> 00:14:45,480 I'm assuming they don't answer the phone at two o'clock in the morning. OK. 134 00:14:45,480 --> 00:14:50,730 It's certainly reasonable to ask whether it's it's just me. 135 00:14:50,730 --> 00:14:55,740 Yes. I mean, there might be something about my particular telephone number that whenever I ring Beattie, 136 00:14:55,740 --> 00:15:00,180 there's something that says, don't answer this one or something like that. 137 00:15:00,180 --> 00:15:05,400 But ask the conclusion is that when I ring Beattie, do you see what I mean? 138 00:15:05,400 --> 00:15:16,110 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. 139 00:15:16,110 --> 00:15:21,960 Yes. I might have only made one or two. I again, that's structurally the same as when we said here. 140 00:15:21,960 --> 00:15:30,480 How many people did we sample in the population and what percentage of that elderly population is that? 141 00:15:30,480 --> 00:15:38,900 And you're suggesting exactly the same thing here. Quite properly. If I've only tried to ring once or twice, then. 142 00:15:38,900 --> 00:15:42,840 Is that really a big enough sample? Good. 143 00:15:42,840 --> 00:15:48,920 Again, you're questioning whether that's premises true. I mean, maybe I'm just very bad at calculating time. 144 00:15:48,920 --> 00:15:54,320 Maybe I'm one of these people who's very keen to get somebody answer my phone call immediately. 145 00:15:54,320 --> 00:15:59,720 And if it takes 30 seconds, then I get very irritated and thinks it's I think it's ours. 146 00:15:59,720 --> 00:16:00,260 OK. 147 00:16:00,260 --> 00:16:11,140 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? 148 00:16:11,140 --> 00:16:15,730 There's Beatty here. Wouldn't mean the same as Beatty here. 149 00:16:15,730 --> 00:16:23,540 OK. An equivocation, by the way, is it is an argument in which you use the same word with two different meanings. 150 00:16:23,540 --> 00:16:27,230 OK. So if you think of the word bank, it could mean financial institution. 151 00:16:27,230 --> 00:16:31,190 It could mean an action of an aeroplane or it could mean the side of a river. 152 00:16:31,190 --> 00:16:35,810 And if in an argument you used it in all three of those meanings, 153 00:16:35,810 --> 00:16:40,400 you could imagine an argument that would look good, but as a matter of fact, wouldn't work at all. 154 00:16:40,400 --> 00:16:45,920 And that's as a result of equivocation. You're equivocating on the word bank. 155 00:16:45,920 --> 00:16:56,030 So if I were equivocating here on the word Beattie or the the letters Beattie, my conclusion might not follow from my premises. 156 00:16:56,030 --> 00:17:00,050 OK. Very good. That really is good. I think it's very impressive. 157 00:17:00,050 --> 00:17:05,220 You'll see as I go through the things that I'm going to list that you've said just about all of them. 158 00:17:05,220 --> 00:17:09,620 OK, firstly, is that just about all of them? This one I think I've got that you haven't. 159 00:17:09,620 --> 00:17:16,880 Is the premise true? Okay, we've got 60 percent of the sample said that they would vote for Mr. 160 00:17:16,880 --> 00:17:22,140 Many promised. Well, can we really believe that? Might they be bad at record keeping? 161 00:17:22,140 --> 00:17:25,550 So it actually wasn't 60 percent. It was only 50 percent. 162 00:17:25,550 --> 00:17:31,100 Then, you know, if if you last year when he used those people, they were completely hopeless. 163 00:17:31,100 --> 00:17:35,450 Might they be engaged in wishful thinking? Might they be bad, just bad at maths? 164 00:17:35,450 --> 00:17:39,140 They can't work out percentages. Am I telling the truth? 165 00:17:39,140 --> 00:17:43,660 Am I in the pay of one of Beattie's rivals? Am I prone to exaggeration? 166 00:17:43,660 --> 00:17:50,610 Am I just very bad at estimating time? So lots of reasons why the premise itself might not be true. 167 00:17:50,610 --> 00:17:56,360 And if you remember, whenever we're evaluating an argument, there are two things we've got to look at. 168 00:17:56,360 --> 00:18:05,220 Can you remember what they are? Just two basic things we look at whenever we're evaluating an argument of any kind at all. 169 00:18:05,220 --> 00:18:12,510 One is, does the conclusion follow from the premises? That's right, and the other is, is the premise are the premises true? 170 00:18:12,510 --> 00:18:16,800 That's right. Is if if even one premise is false, 171 00:18:16,800 --> 00:18:23,130 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. 172 00:18:23,130 --> 00:18:29,890 So first thing you look at when you look at any argument is, are the premises true? 173 00:18:29,890 --> 00:18:35,660 Okay. How large is the sample? Again, you got this. How many of those who would vote in the election were sampled? 174 00:18:35,660 --> 00:18:39,950 10 out of one million? Well, that doesn't look very good, does it? 175 00:18:39,950 --> 00:18:45,180 A thousand out of one million. That looks better. How many is enough, though? 176 00:18:45,180 --> 00:18:49,360 Do you think? And that's a really difficult question, isn't it? 177 00:18:49,360 --> 00:18:58,180 How many is enough? I'm just specifying here that one million is the population. 178 00:18:58,180 --> 00:19:05,170 And then we're saying, OK, how many of those would count is enough? 179 00:19:05,170 --> 00:19:09,700 And I'm saying there actually isn't any answer to that. We can certainly answer that. 180 00:19:09,700 --> 00:19:16,280 Ten is probably not enough. And we might be able to say that. 181 00:19:16,280 --> 00:19:22,350 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? 182 00:19:22,350 --> 00:19:26,150 Okay. Nine hundred thousand would be enough. Okay. 183 00:19:26,150 --> 00:19:34,890 But in between those two numbers, what counts is enough statistics, so. 184 00:19:34,890 --> 00:19:39,300 Well, that's coming later. That's coming when we look at the representativeness of the sample at the moment. 185 00:19:39,300 --> 00:19:47,010 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? 186 00:19:47,010 --> 00:19:52,920 And I say, well, I saw Swan just now and it was white. And you say, what? 187 00:19:52,920 --> 00:20:00,540 Just one. I see. And you can be more or less inductively bold. 188 00:20:00,540 --> 00:20:02,880 And actually, if we were to look at people in this room, 189 00:20:02,880 --> 00:20:08,150 if we were to do a headcount of people in this room, we'd find that some of us are very large. 190 00:20:08,150 --> 00:20:10,860 I shouldn't say ask because I'm I'm not inductively bold. 191 00:20:10,860 --> 00:20:18,450 But some of us would be prepared to extrapolate from a very small number and others of us would be very sceptical about it. 192 00:20:18,450 --> 00:20:25,020 Strapped rating, even from quite a large number. So actually the question, how many is enough? 193 00:20:25,020 --> 00:20:33,880 The answer would be it depends on who you are on and on how inductively bold you are. 194 00:20:33,880 --> 00:20:44,660 Well, statistics statisticians have to come up with something that they would count has enough evidence, right? 195 00:20:44,660 --> 00:20:49,610 Yep. And the larger sample of the competence, right? 196 00:20:49,610 --> 00:20:55,620 They can be more competent to represent. When you say the larger the sample. 197 00:20:55,620 --> 00:21:03,570 Do you mean that? It's certainly true that if the thousands have been samples, that's much more confidence boosting than 10. 198 00:21:03,570 --> 00:21:07,380 Yes, that is. That's what you mean. Yes. Yes. Okay. 199 00:21:07,380 --> 00:21:17,670 Are they saying that as a result that. 80 percent in the election will vote one way plus or minus two percent. 200 00:21:17,670 --> 00:21:23,480 All they're saying. Fifty five percent plus or minus 10 percent. 201 00:21:23,480 --> 00:21:31,680 And they leave the range of their prediction depends upon the size of the sample. 202 00:21:31,680 --> 00:21:38,530 Yes. No, I think, you know, I'm I'm getting out of my depth here. 203 00:21:38,530 --> 00:21:45,830 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. 204 00:21:45,830 --> 00:21:49,510 So, yes, we're coming to that. That's representativeness. Yes. Yes. 205 00:21:49,510 --> 00:22:01,440 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. 206 00:22:01,440 --> 00:22:09,090 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, 207 00:22:09,090 --> 00:22:20,010 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. 208 00:22:20,010 --> 00:22:26,760 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? 209 00:22:26,760 --> 00:22:33,210 It's a very weak argument. Whereas if I've rung Beattie 50 times and not got through. 210 00:22:33,210 --> 00:22:42,360 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. 211 00:22:42,360 --> 00:22:50,610 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. 212 00:22:50,610 --> 00:22:57,840 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. 213 00:22:57,840 --> 00:23:07,350 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. 214 00:23:07,350 --> 00:23:12,870 In other words, six voters out of a million said that they'd be voting for Mr many promise. 215 00:23:12,870 --> 00:23:16,020 Therefore, Mr many promise will win the election. 216 00:23:16,020 --> 00:23:25,950 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. 217 00:23:25,950 --> 00:23:31,560 Therefore, Mr many promise will 60 per cent of people will vote for him in the election and he'll win. 218 00:23:31,560 --> 00:23:35,370 See what I mean? We haven't actually looked at representativeness yet. 219 00:23:35,370 --> 00:23:40,680 We will about to do so. I know you're all dying to get onto on representativeness, so let's do so. 220 00:23:40,680 --> 00:23:47,010 Here we go. OK. The second thing that we ask is how representative is the sample, what you should do instantly. 221 00:23:47,010 --> 00:23:53,250 I'm giving you again, you might say it was another algorithm, another just list of steps that you might do. 222 00:23:53,250 --> 00:23:57,650 Again, try and keep them separate in your mind, because if you tick off each one, OK? 223 00:23:57,650 --> 00:24:02,200 You've asked yourself how many there are in the sample and how many there are in the population. 224 00:24:02,200 --> 00:24:07,770 I made a judgement about whether there is enough in the sample to be able to to extrapolate. 225 00:24:07,770 --> 00:24:14,190 Second question you ask is whether the sample is representative. See what I mean to Descartes. 226 00:24:14,190 --> 00:24:17,750 Very famous philosopher, brilliant philosopher. 227 00:24:17,750 --> 00:24:27,870 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 228 00:24:27,870 --> 00:24:36,180 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. 229 00:24:36,180 --> 00:24:43,390 And what I'm suggesting is you ask each of these questions separately so that you make sure that you ask all of them. 230 00:24:43,390 --> 00:24:52,980 I mean, it just makes your thought clearer. Again, as with first, you identify the arguments and analyse it, then you evaluate it. 231 00:24:52,980 --> 00:24:56,730 OK. And you don't try and do both at once. OK. 232 00:24:56,730 --> 00:25:01,890 So here again, you got all these were the voters sampled all female? 233 00:25:01,890 --> 00:25:12,480 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. 234 00:25:12,480 --> 00:25:15,870 I don't know if you've seen recently they've decided that for women, 235 00:25:15,870 --> 00:25:21,420 the symptoms of a heart attack are quite different from the heart attack symptoms of a man. 236 00:25:21,420 --> 00:25:21,960 And therefore, 237 00:25:21,960 --> 00:25:31,490 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. 238 00:25:31,490 --> 00:25:36,390 There was quite a big thing about that a couple of weeks ago. And are they all over 40? 239 00:25:36,390 --> 00:25:42,000 Are they all white? Are they all middle class? Are they all known to the person conducting the survey, 240 00:25:42,000 --> 00:25:47,640 the famous example that you were mentioning a minute ago and in fact, that you've just mentioned as well. 241 00:25:47,640 --> 00:25:52,860 In an election between who, Roosevelt and London? 242 00:25:52,860 --> 00:25:57,360 That's right. They thought that 60 percent of the population was going to vote. 243 00:25:57,360 --> 00:26:01,320 That's what their sample of said. But how did they find the sample? 244 00:26:01,320 --> 00:26:06,600 They looked in the telephone book. How many people had telephones then? 245 00:26:06,600 --> 00:26:13,260 Actually, very few. So although there was 60 per cent of the sample said that they would vote for Roosevelt. 246 00:26:13,260 --> 00:26:21,420 Actually, the sample was horrendously unrepresentative because it was middle class people with fair amount of money who had telephones. 247 00:26:21,420 --> 00:26:25,830 And therefore, it didn't represent the population as a whole. OK. 248 00:26:25,830 --> 00:26:33,400 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. 249 00:26:33,400 --> 00:26:38,820 Okay, so firstly, it's the premise. True. Secondly, what was it? 250 00:26:38,820 --> 00:26:46,200 How large is the sample as a percentage of the population? Thirdly, how representative is the sample? 251 00:26:46,200 --> 00:26:49,440 Three questions to ask there. Here's another one. 252 00:26:49,440 --> 00:26:57,570 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. 253 00:26:57,570 --> 00:27:07,220 Which one is most likely to come up? And who thinks this one is most likely to come up? 254 00:27:07,220 --> 00:27:15,890 No. OK. Who thinks this one is most likely to come up? No, you're all very clever, aren't you? 255 00:27:15,890 --> 00:27:22,700 You're absolutely right. And they're actually equally likely to come up because, of course, cards are just at random. 256 00:27:22,700 --> 00:27:31,580 They're not. But actually, if if you if you ask the students at the university where this experiment was done, 257 00:27:31,580 --> 00:27:37,230 which hands is likely to come up, they come out overwhelmingly against this one. 258 00:27:37,230 --> 00:27:47,320 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. 259 00:27:47,320 --> 00:27:51,830 This one. Well, yes. This is the one they'd love to have come up. 260 00:27:51,830 --> 00:27:56,900 And this is the one that they have come up. They think all the time sort of thing. 261 00:27:56,900 --> 00:28:05,480 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. 262 00:28:05,480 --> 00:28:09,740 And this always comes up. And actually, you just can't use that here, can you? 263 00:28:09,740 --> 00:28:17,890 Because what comes up is something like that. But certainly not that. 264 00:28:17,890 --> 00:28:21,920 It just means a way of making a decision. OK. 265 00:28:21,920 --> 00:28:26,230 Rule of thumb, if you like. A way of making a decision. Thank you for asking. 266 00:28:26,230 --> 00:28:28,730 I should have explained it before. OK. So. 267 00:28:28,730 --> 00:28:42,470 So if an inductive generalisation is based on on an informal claim like this, in my experience, hands like this never come up. 268 00:28:42,470 --> 00:28:47,390 Therefore this one is is much less likely than that one. 269 00:28:47,390 --> 00:28:51,890 Then you should be very wary of the generalisation. 270 00:28:51,890 --> 00:28:56,090 And here's another one. And I expect you're all going to be clever enough to get this too. 271 00:28:56,090 --> 00:29:03,110 OK. Four pages of a novel. How many words would you expect to find ending in English and in four pages of a novel? 272 00:29:03,110 --> 00:29:09,860 How many words would you expect to find that includes the letter N? Would you expect that to be larger than that? 273 00:29:09,860 --> 00:29:16,900 Or vice versa. So you'd expect more of being words. 274 00:29:16,900 --> 00:29:23,690 No more of the N words. Put up your hands if you think there are more N words. 275 00:29:23,690 --> 00:29:29,700 OK. Put up your hand if you think more in words. OK, that's interesting. 276 00:29:29,700 --> 00:29:39,060 This time you have fallen for the trick because, of course, there are going to be words in words. 277 00:29:39,060 --> 00:29:44,820 That's right. They're always going to have. Oh, okay. I'm sorry. So you're absolutely right. 278 00:29:44,820 --> 00:29:52,560 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. 279 00:29:52,560 --> 00:29:58,320 Yes. Okay. Sorry, you did get that. What happens again when you ask these students, 280 00:29:58,320 --> 00:30:03,600 the psychology students at the university where this experiment was done is they expect 281 00:30:03,600 --> 00:30:11,790 many more of these because they can think of many more in words than they can of N words, 282 00:30:11,790 --> 00:30:17,880 and therefore they they inductively generalise again. Well, I can think of many more of those. 283 00:30:17,880 --> 00:30:22,740 Therefore, there probably are more of those. Again, bad arguments. 284 00:30:22,740 --> 00:30:31,910 If I ask you how many footballers or something from a particular team score, well, you'll be able to think why it is. 285 00:30:31,910 --> 00:30:37,170 That's a very bad example. Anything that you think you know a bit about, 286 00:30:37,170 --> 00:30:41,580 you're probably tempted to rely on your own experience to make an inductive 287 00:30:41,580 --> 00:30:46,200 generalisation that can work if you really do know what you're talking about. 288 00:30:46,200 --> 00:30:56,150 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. 289 00:30:56,150 --> 00:31:09,050 Secure. OK. OK, so five steps there, I think it was when you are evaluating any inductive generalisation you're looking for. 290 00:31:09,050 --> 00:31:15,740 Firstly, is the premise true? Secondly, does the sample size of the population. 291 00:31:15,740 --> 00:31:19,640 Is it large enough compared to the population as a whole? 292 00:31:19,640 --> 00:31:29,630 Thirdly, is the sample representative or is there a bias in it due to whatever all sorts of reasons for different biases? 293 00:31:29,630 --> 00:31:30,950 And finally, 294 00:31:30,950 --> 00:31:42,020 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? 295 00:31:42,020 --> 00:31:50,390 OK. And as I said, all inductive general, all inductive arguments are based on inductive generalisations. 296 00:31:50,390 --> 00:31:55,550 And so that little way of testing things can be used for all of them. 297 00:31:55,550 --> 00:32:02,480 Let's look at causal generalisations. Okay. A causal generalisation is a type of inductive generalisation. 298 00:32:02,480 --> 00:32:12,170 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. 299 00:32:12,170 --> 00:32:20,630 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. 300 00:32:20,630 --> 00:32:26,060 You extrapolate to the claim that A's and B's will always be correlated. 301 00:32:26,060 --> 00:32:31,700 And you imply that the reason for this is that there's a causal relation between them. 302 00:32:31,700 --> 00:32:38,120 So where there's correlation, there's cause that sort of causal generalisation is. 303 00:32:38,120 --> 00:32:43,700 So let's have a look at a couple. Okay. Married men live longer than single men. 304 00:32:43,700 --> 00:32:49,370 Therefore, being married causes you to live longer. I apologise for this one. 305 00:32:49,370 --> 00:32:57,450 When error is allowed into a wound magots form. Therefore, maggots in wounds are caused by air being allowed into the womb wound. 306 00:32:57,450 --> 00:33:02,690 This is. Sorry. Okay. 307 00:33:02,690 --> 00:33:11,760 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. 308 00:33:11,760 --> 00:33:15,030 Okay, let's have a look. Again, we ask, is the premise true? 309 00:33:15,030 --> 00:33:20,340 Who says, man, married men live longer? Married men. A woman who wants to get married. 310 00:33:20,340 --> 00:33:23,730 Fred, whose parents split up when he was five. I mean, who who's saying this? 311 00:33:23,730 --> 00:33:28,830 Where are we actually getting this information from? Who says magots form when I get since the womb? 312 00:33:28,830 --> 00:33:34,080 Just as you said at the back there. Was it a newly qualified nurse who Soos observed this once? 313 00:33:34,080 --> 00:33:41,310 Was it an elderly doctor who is seen as a lot, but only in his own experience and in his own study, perhaps? 314 00:33:41,310 --> 00:33:48,680 Or was it a scientific study of one that you would expect to be to have looked more carefully? 315 00:33:48,680 --> 00:33:56,090 Causation is actually I mean, to give you a little bit of background on this. 316 00:33:56,090 --> 00:34:05,120 David Hume, the person I've mentioned already in connexion with the principle of the uniformity of nature, believes that actually causation. 317 00:34:05,120 --> 00:34:14,690 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? 318 00:34:14,690 --> 00:34:20,150 What is it that relates the two things that would cause and effect? 319 00:34:20,150 --> 00:34:24,440 We'll just find another correlation. CND. Okay. 320 00:34:24,440 --> 00:34:30,380 So why do we think C and D are correlated? We look further and we look down and we see yet another correlation. 321 00:34:30,380 --> 00:34:36,230 So all we ever see is correlation. We never actually see the causal relation itself. 322 00:34:36,230 --> 00:34:39,560 We can never get to the causal relations itself. 323 00:34:39,560 --> 00:34:46,310 And he actually thought arguably this is a very popular theory of Hume, although lots of people deny it. 324 00:34:46,310 --> 00:34:50,630 These days that he actually thought causation didn't exist at all. 325 00:34:50,630 --> 00:34:55,550 That causes that are beliefs about causation or just the habit of mind. 326 00:34:55,550 --> 00:35:00,290 So we see a correlation would be a correlate, it would be A correlated with B, 327 00:35:00,290 --> 00:35:05,450 and we start to say that A causes B, and all we mean by that is that A is correlated. 328 00:35:05,450 --> 00:35:11,360 B, there's just a constant conjunction between A and B, there's nothing that makes A cause. 329 00:35:11,360 --> 00:35:20,960 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, 330 00:35:20,960 --> 00:35:29,420 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. 331 00:35:29,420 --> 00:35:32,690 And that suggests that there is a power of some kind, isn't there? 332 00:35:32,690 --> 00:35:39,080 That makes A cause B, but but we do not have a C that power, do we. 333 00:35:39,080 --> 00:35:45,180 We don't. We just see the cause and the effect and the correlation between them. 334 00:35:45,180 --> 00:35:50,280 And so causation is is a really interesting philosophical issue. 335 00:35:50,280 --> 00:35:55,160 The question what's causation is, is endlessly interesting. 336 00:35:55,160 --> 00:36:02,730 I think it's endlessly interesting, but it remains to be the case that our evidence for causation is always a correlation, 337 00:36:02,730 --> 00:36:11,670 but a correlation simply isn't sufficient as evidence for court of causation, is it because it could be evidence for identity, for example. 338 00:36:11,670 --> 00:36:17,360 So that night. Well, the evening star goes down. 339 00:36:17,360 --> 00:36:21,870 Morning star rises and so on. So forth. Do they cause each other to do it? 340 00:36:21,870 --> 00:36:26,640 No, actually, they're the same thing. That's why they're correlated. 341 00:36:26,640 --> 00:36:32,640 That's why the pattern is uniform. Do husbands cause wives? 342 00:36:32,640 --> 00:36:35,130 But they're correlated. 343 00:36:35,130 --> 00:36:43,500 Well, what we're saying is that correlation isn't sufficient for a causation, but it's the only evidence wherever likely to have. 344 00:36:43,500 --> 00:36:49,470 But when you when you see a causal generalisation, it will be based on correlations. 345 00:36:49,470 --> 00:36:54,780 But what we're alerting you to here is that a correlation isn't sufficient for a causation. 346 00:36:54,780 --> 00:36:58,770 You need to ask lots of other questions. So is the premise true? 347 00:36:58,770 --> 00:37:02,430 How strong is the correlation? How many married men were observed? 348 00:37:02,430 --> 00:37:07,650 I mean, this is, again, exactly the same as how many are in the sample from the last question. 349 00:37:07,650 --> 00:37:14,790 How long were they observed? Were unmarried men observed? How many cases of maggots forming were observed? 350 00:37:14,790 --> 00:37:21,360 And because when John Stuart Mill, famous philosopher, English philosopher, 351 00:37:21,360 --> 00:37:28,560 came up with what he called the method of agreement and the method of difference for scientific experiments. 352 00:37:28,560 --> 00:37:32,580 What if you're trying to work out what causes what you need to see? 353 00:37:32,580 --> 00:37:38,310 Firstly, that they do correlate that, that the cause correlates with the effect. 354 00:37:38,310 --> 00:37:43,800 Next thing you need to do is to try and bring about the cause without the effect. 355 00:37:43,800 --> 00:37:49,740 Because if you're saying that is cause B because all A's are always correlated with B, 356 00:37:49,740 --> 00:37:59,520 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. 357 00:37:59,520 --> 00:38:07,830 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, 358 00:38:07,830 --> 00:38:15,060 the occurrence of an A must be followed by the occurrence of a B because A is sufficient for B. 359 00:38:15,060 --> 00:38:24,050 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. 360 00:38:24,050 --> 00:38:29,820 And also, you want to ask, does the causal relation make sense or could it be accidental? 361 00:38:29,820 --> 00:38:41,650 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. 362 00:38:41,650 --> 00:38:47,070 OK. We have a correlation and we've done our very best to try and make sure that we've struck matches 363 00:38:47,070 --> 00:38:54,150 without a pineapple falling and keep on doing it so we can't break the correlation in any way. 364 00:38:54,150 --> 00:39:01,220 Do we think that match is striking cause pineapple's to fall? 365 00:39:01,220 --> 00:39:08,180 Well, some people are quite inductively bold here, they think, yes, if you've got correlation as strong as that, it must be causal. 366 00:39:08,180 --> 00:39:14,330 Apparently, there's also a correlation between the length of skirts and the Dow Jones index. 367 00:39:14,330 --> 00:39:17,870 As one goes up, the other goes up. And this one goes down. The other goes down. 368 00:39:17,870 --> 00:39:21,230 It might be the other way round. But anyway, there's a correlation here. 369 00:39:21,230 --> 00:39:27,530 Do we think that the length of skirts causes the rise and fall of the Dow Jones index or vice versa? 370 00:39:27,530 --> 00:39:33,590 That's more likely. You can sort of see something that makes sense. 371 00:39:33,590 --> 00:39:39,710 Can't you, in that? Because you may be when the down to Jones index is really high. 372 00:39:39,710 --> 00:39:45,230 People are really excited and pleased and therefore they risk taking risk taking. 373 00:39:45,230 --> 00:39:51,520 So they put on their mini skirt. Okay. 374 00:39:51,520 --> 00:39:56,030 It does. Okay. So the claim that being married makes you live longer if you're a man. 375 00:39:56,030 --> 00:39:59,270 Why? Why would being married cause men to live longer? 376 00:39:59,270 --> 00:40:04,920 I think this is where your claim about all we, including civil relationships, is quite interesting. 377 00:40:04,920 --> 00:40:11,390 Why would being married cause men to live longer? Certainly, I think it causes women die earlier. 378 00:40:11,390 --> 00:40:17,950 Just a warning to women and through. Okay, they're happier. 379 00:40:17,950 --> 00:40:22,430 The stress reduces another explanation. Well, that's a first. 380 00:40:22,430 --> 00:40:29,480 He. It might also be because women tend to look after diets and things like that. 381 00:40:29,480 --> 00:40:33,530 More men are cooks for more often than women are perhaps. 382 00:40:33,530 --> 00:40:36,740 And when women do the cooking, they concerned about nutrition and da da da da. 383 00:40:36,740 --> 00:40:44,180 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? 384 00:40:44,180 --> 00:40:51,830 So it's not a complete mystery. What about this one? 385 00:40:51,830 --> 00:40:55,340 Why would Air getting into a wound cause maggots to form so. 386 00:40:55,340 --> 00:41:03,620 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, 387 00:41:03,620 --> 00:41:08,420 maggots didn't form. And she thinks, well, you know, could it be so? 388 00:41:08,420 --> 00:41:14,270 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, 389 00:41:14,270 --> 00:41:20,030 whereas the ones that left open do get maggots. So she's formed a hypothesis. 390 00:41:20,030 --> 00:41:28,340 Could it be that getting into the wound causes maggots? But why would that be the case? 391 00:41:28,340 --> 00:41:32,570 Perhaps because there's something carried in the air that causes maggots to form. 392 00:41:32,570 --> 00:41:36,620 And actually, we know now that that is the case. So, okay. 393 00:41:36,620 --> 00:41:43,700 So what does the causal relation make sense? Incidentally, if it doesn't make sense, does that mean it's not causal? 394 00:41:43,700 --> 00:41:51,540 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. 395 00:41:51,540 --> 00:41:57,230 And I wish I could think of an example, but which turns out to be true and turns out to have an explanation. 396 00:41:57,230 --> 00:42:02,690 But even so, if you can't if if it just if these things seem to be just totally disparate, 397 00:42:02,690 --> 00:42:08,350 that would be a mark against this argument being a good one. 398 00:42:08,350 --> 00:42:15,650 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? 399 00:42:15,650 --> 00:42:20,660 So it might be that having genes for longevity caused men to get married. 400 00:42:20,660 --> 00:42:26,210 So you said socio economic factors. But I'm suggesting could be genetic factors. 401 00:42:26,210 --> 00:42:34,250 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. 402 00:42:34,250 --> 00:42:43,920 So there's one common cause for the two things, rather that one thing causes the other. 403 00:42:43,920 --> 00:42:49,140 And that I couldn't think of anything could Magots forming cause to get into the womb? 404 00:42:49,140 --> 00:42:53,650 No, I couldn't think about that, so. Okay. 405 00:42:53,650 --> 00:43:00,870 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 406 00:43:00,870 --> 00:43:05,880 generalisations are also questions you've already asked about inductive generalisations. 407 00:43:05,880 --> 00:43:13,200 That's not surprising because causal generalisations are a type of inductive generalisation. 408 00:43:13,200 --> 00:43:23,490 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? 409 00:43:23,490 --> 00:43:33,270 Say that. So just moving on quickly to an analogy here, another type of inductive generalisation, 410 00:43:33,270 --> 00:43:42,060 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. 411 00:43:42,060 --> 00:43:47,040 And there's a famous argument from analogy. The universe is like a pocket watch. 412 00:43:47,040 --> 00:43:51,780 Pocket watches have designers. Therefore, the universe must have a designer. 413 00:43:51,780 --> 00:43:56,310 I think we're probably all familiar with that argument. Okay. 414 00:43:56,310 --> 00:44:05,230 How would we go about questioning this argument? Yes. 415 00:44:05,230 --> 00:44:10,690 OK. And what aspects are we picking out here and saying is similar to the two cases? 416 00:44:10,690 --> 00:44:17,370 So why is the universe like a pocketwatch? I mean, using this famous example, what did the person believe? 417 00:44:17,370 --> 00:44:22,930 Was it complicated? Lover. That's right. 418 00:44:22,930 --> 00:44:26,830 It was very. Who was it? It's gone completely, palely. 419 00:44:26,830 --> 00:44:32,020 That's right. Thank you. I'm sorry. I have got a head full of cotton wool. It's very strange. 420 00:44:32,020 --> 00:44:36,850 Yeah. Paly believed that the universe the pocket watches is moves regularly. 421 00:44:36,850 --> 00:44:42,310 It's very complex. It's. It must be very difficult to put together. 422 00:44:42,310 --> 00:44:46,360 And he believes that the universe is also very regular, very complex. 423 00:44:46,360 --> 00:44:51,880 It must have been difficult to put together. Therefore, if one has a designer, the other has a designer. 424 00:44:51,880 --> 00:44:56,070 What else might you ask? Okay. There are many, though. 425 00:44:56,070 --> 00:45:00,160 There is a similarity, we might say, between pocket watches and the universe. 426 00:45:00,160 --> 00:45:03,610 But there are many, many dissimilarities. Why? 427 00:45:03,610 --> 00:45:08,410 Why should we consider that this similarity is more important than all these differences? 428 00:45:08,410 --> 00:45:10,240 Yep. Okay. 429 00:45:10,240 --> 00:45:19,540 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, 430 00:45:19,540 --> 00:45:31,320 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. 431 00:45:31,320 --> 00:45:39,770 OK, but we are saying that the universe is like a pocket watch in being very complex and regular pocket watches have a designer. 432 00:45:39,770 --> 00:45:43,970 Oh, I see. Okay, so you're absolutely right. 433 00:45:43,970 --> 00:45:52,630 I'm sorry. No, you are right. I was I was changing that second premise to everything that's complicated has a designer. 434 00:45:52,630 --> 00:45:57,760 And that's not what it says, is it? And so I've rightly been pulled up on that. 435 00:45:57,760 --> 00:46:05,730 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? 436 00:46:05,730 --> 00:46:13,500 In order to give an argument, we do have to say a lot of things in support of the various premises and in 437 00:46:13,500 --> 00:46:17,220 support of our belief that the conclusion follows from the premise and so on. 438 00:46:17,220 --> 00:46:21,300 So you wouldn't expect almost anything said to be an argument. 439 00:46:21,300 --> 00:46:24,930 And actually, as you learn. Yes. 440 00:46:24,930 --> 00:46:32,760 No, I'm not surprised. I'm mean, I suppose what I'm doing is I'm defending newspapers because actually you need to 441 00:46:32,760 --> 00:46:39,600 read a whole or a whole article in order to see what the claim being made is. 442 00:46:39,600 --> 00:46:44,830 And then you need to go back and identify what the reasons are being given for the claim. 443 00:46:44,830 --> 00:46:54,090 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. 444 00:46:54,090 --> 00:46:56,520 And also, can we find a decent allergy, 445 00:46:56,520 --> 00:47:04,050 which is the thing you mentioned is are there differences between them and do the differences pertain to this argument? 446 00:47:04,050 --> 00:47:11,430 But the thing to remember about arguments from analogy is that they are extrapolating from just one example. 447 00:47:11,430 --> 00:47:19,860 Therefore, the one example and the extrapolation have to be really pretty strong before you should go along with them. 448 00:47:19,860 --> 00:47:27,660 So arguments from are actually arguments from analogy are much more common and probably for the reason you're saying, 449 00:47:27,660 --> 00:47:31,830 because they often take us along with them emotionally. 450 00:47:31,830 --> 00:47:41,910 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. 451 00:47:41,910 --> 00:47:45,810 And they extrapolate to the claim that they're right about other things. 452 00:47:45,810 --> 00:47:51,510 So human rights monitoring organisations are experts on whether human rights have been violated. 453 00:47:51,510 --> 00:47:58,470 They say that some prisoners are mistreated tonight in Mexico. Therefore, some prisoners are mistreated in Mexico. 454 00:47:58,470 --> 00:48:04,950 What do we need to ask about this? Where do they get their information from? 455 00:48:04,950 --> 00:48:10,830 Is it just that they've become hackneyed and cynical and they think that everyone mistreats everyone? 456 00:48:10,830 --> 00:48:15,280 Or do they actually have reasons for saying what they have? Yep. 457 00:48:15,280 --> 00:48:21,730 I mean, all is needed for this argument is that some prisoners are mistreated. Not that they're mistreated by anyone in particular. 458 00:48:21,730 --> 00:48:30,450 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, 459 00:48:30,450 --> 00:48:35,100 but are they experts on whether somebody is been mistreated or are they perhaps 460 00:48:35,100 --> 00:48:41,730 seeing trivial forms of mistreatment as violations of human rights or something? 461 00:48:41,730 --> 00:48:47,890 Is that what you mean? Yep. Okay. Okay. Well, let's have a look at the. 462 00:48:47,890 --> 00:48:50,730 Okay. Who exactly is the source of information? 463 00:48:50,730 --> 00:48:58,950 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. 464 00:48:58,950 --> 00:49:06,430 And again, there you would make you'd want to make a judgement about whether the source of information really is an expert, 465 00:49:06,430 --> 00:49:11,850 whether they they're qualified in the appropriate area, because it's very easy. 466 00:49:11,850 --> 00:49:20,430 Again, going back to how inductively bold you are, if you have a tendency to think this person is an expert in one area, 467 00:49:20,430 --> 00:49:28,290 you may well inductively generalise to his or her being an expert in another area. 468 00:49:28,290 --> 00:49:36,330 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. 469 00:49:36,330 --> 00:49:46,550 But if what she's talking about is politics or mathematics or something like that, that is complete nonsense, isn't it? 470 00:49:46,550 --> 00:49:49,740 Okay. So so not only that, you need to know who they are. 471 00:49:49,740 --> 00:49:57,360 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. 472 00:49:57,360 --> 00:50:02,220 So Amnesty International, let's say, are impartial. 473 00:50:02,220 --> 00:50:06,480 They go out and they get the evidence and they're very careful not to be biased. 474 00:50:06,480 --> 00:50:09,990 I don't know whether that's true and certainly. But let's say it could be. 475 00:50:09,990 --> 00:50:16,950 But then there might be another human rights organisation that's not careful to make sure that its information isn't biased. 476 00:50:16,950 --> 00:50:24,670 So you'd need to make a distinction between the fact that amnesty is is a reputable organisation and this other one isn't. 477 00:50:24,670 --> 00:50:34,240 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, 478 00:50:34,240 --> 00:50:38,980 one way of doing it is to say that the people who are who are putting forward this survey are biased. 479 00:50:38,980 --> 00:50:43,070 So I've been working on. Look, is GM food. 480 00:50:43,070 --> 00:50:52,660 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 481 00:50:52,660 --> 00:50:58,810 that isn't that that's the sort of the soil association or somebody that's very anti GM food. 482 00:50:58,810 --> 00:51:03,790 So finding something that really is an impartial source is really very difficult. 483 00:51:03,790 --> 00:51:10,420 And it's very, very important to to try and find one if you're really going to evaluate these arguments. 484 00:51:10,420 --> 00:51:18,730 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 485 00:51:18,730 --> 00:51:24,220 rarely the case also that all the experts in an area will agree on on something. 486 00:51:24,220 --> 00:51:31,450 And if you have different experts making different claims, you need to make a judgement as to where you think. 487 00:51:31,450 --> 00:51:35,500 Which of them do you think is is correct and what you cannot rely on? 488 00:51:35,500 --> 00:51:40,960 There is an argument from authority, can you? Because they're both authorities. 489 00:51:40,960 --> 00:51:48,490 So if you were an undergraduate writing an essay or indeed if you were you writing an essay on philosophy for me, 490 00:51:48,490 --> 00:51:58,700 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. 491 00:51:58,700 --> 00:52:04,790 So-and-so says this and Thingamabobs says that, and he says P and he says not P. 492 00:52:04,790 --> 00:52:10,220 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, 493 00:52:10,220 --> 00:52:16,550 that thingummy Bob Gibbs and work out which ones you think are the best ones and why. 494 00:52:16,550 --> 00:52:25,220 OK. So there's no substitute for thinking for yourself. An appeal to an argument for authority is okay for for various things. 495 00:52:25,220 --> 00:52:28,280 I mean, we have to rely on authorities for all sorts of things. 496 00:52:28,280 --> 00:52:36,560 But if you were trying to write a philosophy essay saying Marijan says P, therefore P will not do. 497 00:52:36,560 --> 00:52:46,520 And that's true of every philosopher you ever come across. Because there are very, very few things in philosophy that aren't questioned. 498 00:52:46,520 --> 00:52:51,590 Okay. That's where I was going today. Next week, we'll look at validity and truth. 499 00:52:51,590 --> 00:52:58,736 And then we'll turn to the evaluation of deductive arguments.