1 00:00:02,540 --> 00:00:06,562 Last time we talked about test for sufficient conditions and necessary 2 00:00:06,562 --> 00:00:09,452 conditions. And those times will work fine in a lot 3 00:00:09,452 --> 00:00:13,815 of cases, but they're not going to work in all cases because remember we were 4 00:00:13,815 --> 00:00:17,724 looking at what caused death. Death well you're either dead or you're 5 00:00:17,724 --> 00:00:20,387 not dead. When you're talking about whether the 6 00:00:20,387 --> 00:00:24,920 person had fish or not, we didn't look at cases for people who had just a little 7 00:00:24,920 --> 00:00:27,866 bit of fish. They either ate fish or they didn't eat 8 00:00:27,866 --> 00:00:31,050 fish. But there are a lot of causal relations 9 00:00:31,050 --> 00:00:35,183 that hold, not between absolute or dichotomous properties. 10 00:00:35,183 --> 00:00:40,041 You're either dead or not dead. You ate fish or you didn't eat fish. 11 00:00:40,041 --> 00:00:45,479 Instead, some causal relations hold between, properties that come in degrees. 12 00:00:45,479 --> 00:00:49,750 For example. Carbon dioxide and global warming. 13 00:00:49,750 --> 00:00:54,727 Well, there's always carbon dioxide. The atmosphere always has some of it. 14 00:00:54,727 --> 00:01:00,120 So you can't say that carbon dioxide is sufficient for causing heating in the 15 00:01:00,120 --> 00:01:05,374 world, since sometimes the world cools down, and there's still carbon dioxide 16 00:01:05,374 --> 00:01:08,537 then. So, what we need to think about is the 17 00:01:08,537 --> 00:01:12,764 degree of carbon dioxide. And sure enough that's what the 18 00:01:12,764 --> 00:01:17,881 intergovernmental panel on climate change, the IPCC used to reach its 19 00:01:17,881 --> 00:01:23,516 conclusion, that increasing levels of carbon dioxide were causing increasing 20 00:01:23,516 --> 00:01:27,571 global temperatures. We can't use the sufficient condition 21 00:01:27,571 --> 00:01:32,917 test or the necessary condition test. But we can still reach a conclusion about 22 00:01:32,917 --> 00:01:37,180 causation and we can even know the mechanism for the causation. 23 00:01:37,180 --> 00:01:42,526 The carbon monoxide reflects the heat from the sun back to the earth, and traps 24 00:01:42,526 --> 00:01:45,639 it in like a greenhouse effect as it's called. 25 00:01:45,639 --> 00:01:50,647 So the question for this lecture is, how we going to use arguments to justify 26 00:01:50,647 --> 00:01:55,239 those kinds of causal claims? So, let's look at another example. 27 00:01:55,239 --> 00:02:01,293 Suppose a runner runs a mile and take him ten minutes, why were they so slow? 28 00:02:01,293 --> 00:02:04,640 Well, maybe because they were really heavy. 29 00:02:04,640 --> 00:02:09,429 Now, weight can't be sufficient for running slow, because everybody's got a 30 00:02:09,429 --> 00:02:12,859 certain weight. So it's not an on-off property, it's a 31 00:02:12,859 --> 00:02:17,324 property that comes in degrees. And again, the heavier, the harder it's 32 00:02:17,324 --> 00:02:20,884 going to be to run. We know the mechanism, because it takes 33 00:02:20,884 --> 00:02:25,427 more energy to move a heavy body. So, we've got a pretty good causal story, 34 00:02:25,427 --> 00:02:29,759 and a pretty good causal hypothesis. But you can't use the necessary and 35 00:02:29,759 --> 00:02:34,393 sufficient condition test because we're dealing with properties that come in 36 00:02:34,393 --> 00:02:37,101 degrees. A third example, suppose the wages go 37 00:02:37,101 --> 00:02:40,170 down, well that's because the unemployment was high. 38 00:02:40,170 --> 00:02:45,924 There's always some unemployment, wages are never as high as you'd like 'em to 39 00:02:45,924 --> 00:02:48,580 be. So, how do you get a causal claim. 40 00:02:48,580 --> 00:02:53,028 Well, it's because the more unemployment there is, the lower the wages are. 41 00:02:53,028 --> 00:02:58,025 Cause if a lot of people are unemployed their willing to take jobs for a lot less 42 00:02:58,025 --> 00:03:00,579 wage. So we've got a causal story, we've got a 43 00:03:00,579 --> 00:03:03,559 causal claim. They seem to be justified, but you can't 44 00:03:03,559 --> 00:03:06,258 use the necessary and sufficient condition test. 45 00:03:06,258 --> 00:03:10,249 You need a different test. And the test you need has to deal with 46 00:03:10,249 --> 00:03:15,047 properties that come in degrees. And the test that does that is called the 47 00:03:15,047 --> 00:03:19,716 method of concomitant variation. It was developed by John Stuart Mill in 48 00:03:19,716 --> 00:03:22,634 the nineteenth century, brilliant philosopher. 49 00:03:22,634 --> 00:03:27,627 He also developed tests that basically what we've been calling the necessary 50 00:03:27,627 --> 00:03:31,388 condition test and the sufficient condition test, parallel. 51 00:03:31,388 --> 00:03:35,150 He gave them different names but it's the same basic idea. 52 00:03:35,150 --> 00:03:39,447 This lecture's on the method of concomitant or concomitant variation, 53 00:03:39,447 --> 00:03:43,247 also developed by John Stuart Mill in the nineteenth century. 54 00:03:43,247 --> 00:03:48,043 And in order to apply the method of concomitant or concomitant variation, the 55 00:03:48,043 --> 00:03:52,590 first thing we've got to get straight is the idea of what concomitant or 56 00:03:52,590 --> 00:03:56,764 concomitant variation is. Well to avoid that word people these days 57 00:03:56,764 --> 00:04:00,688 call them correlations. The first kind of correlation we've got 58 00:04:00,688 --> 00:04:06,619 to understand is a positive correlation. X and Y are positively correlated when an 59 00:04:06,619 --> 00:04:10,713 increase in X is associated with an increase in Y. 60 00:04:10,713 --> 00:04:14,808 A decease in X is associated with a decrease in Y. 61 00:04:14,808 --> 00:04:20,874 That's what a positive correlation is. Contrast a negative correlation is when 62 00:04:20,874 --> 00:04:26,413 an increase in x is associated with a decrease in y, and a decrease in x is 63 00:04:26,413 --> 00:04:31,583 associated with an increase in y. That's further negatively correlated 64 00:04:31,583 --> 00:04:37,418 cause increases associated with decrease with the possible correlated with the 65 00:04:37,418 --> 00:04:43,105 increase and increase is associated with decrease and decrease is associated. 66 00:04:43,105 --> 00:04:46,560 So let's see some examples. First of all. 67 00:04:46,560 --> 00:04:51,894 Calorie intake is associated with weight. The more calories you take in, the more 68 00:04:51,894 --> 00:04:55,962 weight you're going to gain. So they're positively correlated. 69 00:04:55,962 --> 00:04:59,563 Whereas weight and exercise are negatively correlated. 70 00:04:59,563 --> 00:05:04,497 The more exercise, the less weight because it burns off those calories you 71 00:05:04,497 --> 00:05:07,831 took in. So you get a negative correlation between 72 00:05:07,831 --> 00:05:12,766 weight and exercise and a positive correlation between weight and calorie 73 00:05:12,766 --> 00:05:14,640 intake. Another example. 74 00:05:14,640 --> 00:05:21,054 Height and age are positively correlated before the age of twenty, cause people 75 00:05:21,054 --> 00:05:27,019 tend to get taller as they grow older. The height and age are negatively 76 00:05:27,019 --> 00:05:33,213 correlated after the age of 60, because the space between people's vertebrae tend 77 00:05:33,213 --> 00:05:38,637 to decrease after a certain age. So this shows that two things can be 78 00:05:38,637 --> 00:05:43,627 positively correlated in some circumstances and negatively correlated 79 00:05:43,627 --> 00:05:46,906 in others. Which correlation holds is going to 80 00:05:46,906 --> 00:05:51,041 depend on the circumstances, that is how old the person is. 81 00:05:51,041 --> 00:05:56,602 And now the next question is how can we get from these correlations to causal 82 00:05:56,602 --> 00:05:59,596 relations. But two features are correlated, 83 00:05:59,596 --> 00:06:05,085 positively or negatively, then there's four possible causal relations between 84 00:06:05,085 --> 00:06:07,010 them. First A might cause B. 85 00:06:07,010 --> 00:06:12,862 Sometimes when A and B are correlated, that means that A is causing some kind of 86 00:06:12,862 --> 00:06:16,932 change in B. Second possibility, is that B causes A. 87 00:06:16,932 --> 00:06:21,772 because the correlation's symmetrical. If A is correlated with B, B's correlated 88 00:06:21,772 --> 00:06:24,934 with A. And then we don't know whether A causes B 89 00:06:24,934 --> 00:06:27,644 or B causes A. But those are two different 90 00:06:27,644 --> 00:06:33,100 possibilities, and we will have to see how to distinguish them, in a minute. 91 00:06:33,100 --> 00:06:37,836 But the third possibility is that some third thing, C, causes both A and B. 92 00:06:37,836 --> 00:06:42,896 If C causes B, and C also causes A, then A and B are going to be correlated.'Cause 93 00:06:42,896 --> 00:06:48,086 whenever C is there, you get both of'em. And when C is not there, unless something 94 00:06:48,086 --> 00:06:52,044 [INAUDIBLE] causes them. Then they're not going to be there, So A 95 00:06:52,044 --> 00:06:57,720 and B will be correlated, simply because, a third thing, C, is causing them both. 96 00:06:57,720 --> 00:07:02,855 And the fourth possibility is that the correlation is purely accidental. 97 00:07:02,855 --> 00:07:07,961 A and B just happened to change together. Okay, so here's some examples. 98 00:07:07,961 --> 00:07:11,948 First of all let's do an example of A causing B, okay? 99 00:07:11,948 --> 00:07:17,664 The speed of driving is positively correlated with automobile accidents and 100 00:07:17,664 --> 00:07:23,682 deaths from automobile accidents because when you're going very quickly in your 101 00:07:23,682 --> 00:07:27,518 car and have an accident, you're more likely to die. 102 00:07:27,518 --> 00:07:32,408 Okay, which causes which? It's the speed that causes the accidents 103 00:07:32,408 --> 00:07:36,244 and the deaths. The death didn't cause you to drive 104 00:07:36,244 --> 00:07:39,780 faster before you died, that seems pretty clear. 105 00:07:39,780 --> 00:07:43,472 Okay, now what about the second possibility, B causes A? 106 00:07:43,472 --> 00:07:47,370 Well it's just the reverse, you can use the same examples. 107 00:07:47,370 --> 00:07:52,636 You can say automobile accidents and deaths are correlated with fast driving, 108 00:07:52,636 --> 00:07:56,192 with speed. And in that case it's not that the thing 109 00:07:56,192 --> 00:08:01,185 we mentioned first causes the thing we mention second, it's the other way 110 00:08:01,185 --> 00:08:04,330 around. Because as we've said before, the speed 111 00:08:04,330 --> 00:08:09,460 that causes the automobile accidents and the deaths, not the deaths and the 112 00:08:09,460 --> 00:08:13,920 accidents that cause the speed. 'Kay? 113 00:08:13,920 --> 00:08:19,880 What about the third possibility, that some third thing causes them both? 114 00:08:19,880 --> 00:08:24,573 Well, here's an example. Having yellow teeth is correlated with 115 00:08:24,573 --> 00:08:30,024 having lung cancer, well why is that? Cause there's some third thing that 116 00:08:30,024 --> 00:08:35,626 causes people's teeth to become yellow and also causes lung cancer, namely 117 00:08:35,626 --> 00:08:39,412 smoking. So people who smoke tend to have yellower 118 00:08:39,412 --> 00:08:43,197 teeth and they also tend to have more lung cancer. 119 00:08:43,197 --> 00:08:48,180 So those are correlated because smoking causes both of them. 120 00:08:48,180 --> 00:08:52,276 'Kay. Another example, in young children, shoe 121 00:08:52,276 --> 00:08:57,135 size is correlated, with the quality of handwriting. 122 00:08:57,135 --> 00:09:02,755 As people's shoes get bigger their handwriting gets better. 123 00:09:02,755 --> 00:09:07,565 Now why is that? It's because they're maturing and as 124 00:09:07,565 --> 00:09:12,936 their bodies mature, their feet get bigger and also their handwriting gets 125 00:09:12,936 --> 00:09:16,274 better. Well, so now we have an example of each 126 00:09:16,274 --> 00:09:20,266 of the first three types of cases that we talked about. 127 00:09:20,266 --> 00:09:23,750 What about the fourth? Correlation is accidental. 128 00:09:23,750 --> 00:09:27,898 Well, here's an example. The height of my son and the height of 129 00:09:27,898 --> 00:09:31,077 the tree outside the window, well their correlated. 130 00:09:31,077 --> 00:09:34,128 The taller my son gets, the taller the tree gets. 131 00:09:34,128 --> 00:09:37,433 And the taller the tree gets, the taller my son gets. 132 00:09:37,433 --> 00:09:40,230 Their symmetrical, their correlated together. 133 00:09:40,230 --> 00:09:46,147 Does that mean that something about the height of my son causes the height of the 134 00:09:46,147 --> 00:09:46,724 tree? No. 135 00:09:46,724 --> 00:09:47,951 Or visa versa. No. 136 00:09:47,951 --> 00:09:52,425 Well you might say there's some third cause namely naturation. 137 00:09:52,425 --> 00:09:58,198 But unlike the case of handwriting and size of feet where it was the naturation 138 00:09:58,198 --> 00:10:00,580 of the same person. The same body. 139 00:10:00,580 --> 00:10:06,064 Here my son's body maturing and the tree's body maturing are just unrelated. 140 00:10:06,064 --> 00:10:11,476 You might say that time is the third cause that makes my son grow and also 141 00:10:11,476 --> 00:10:15,641 makes the tree grow. But notice that time is an abstract thing 142 00:10:15,641 --> 00:10:19,140 that's always there and always moves at the same rate. 143 00:10:19,140 --> 00:10:24,389 So it can't really cause, my son to grow at this particular moment or the tree to 144 00:10:24,389 --> 00:10:28,990 grow at this particular moment. It's a background condition, rather than 145 00:10:28,990 --> 00:10:33,688 a cause in and of itself. So now we've got four possibilities and 146 00:10:33,688 --> 00:10:38,613 we've got examples of each. The question that we have to face is, 147 00:10:38,613 --> 00:10:43,076 fine, all four are possible. How do you tell which of these 148 00:10:43,076 --> 00:10:47,540 possibilities is the one the applies in a particular case? 149 00:10:47,540 --> 00:10:52,626 That's going to be tricky. So the problem is, how can we tell which 150 00:10:52,626 --> 00:10:57,066 of the four possibilities applies in a particular case? 151 00:10:57,066 --> 00:11:02,475 And one simple rule is that, when A causes B, A has to come before B. 152 00:11:02,475 --> 00:11:07,965 So if the first possibility is instantiated, then you have to have A 153 00:11:07,965 --> 00:11:11,921 coming before B. But if the second possibility is 154 00:11:11,921 --> 00:11:16,500 instantiated, then you have to have B coming before A. 155 00:11:16,500 --> 00:11:22,754 Now, in the last two, you can have them on any temporal relation you want. 156 00:11:22,754 --> 00:11:29,356 But if a comes before b, then you know that b doesn't cause a and if b comes 157 00:11:29,356 --> 00:11:33,352 before a, then you know that a doesn't cause b. 158 00:11:33,352 --> 00:11:38,130 So, for example, exercise is correlated with weight loss. 159 00:11:38,130 --> 00:11:42,295 Could it be that the weight loss causes the exercise? 160 00:11:42,295 --> 00:11:48,504 Well, it's possible I suppose, but if the exercise occurs before the weight loss, 161 00:11:48,504 --> 00:11:54,084 then we know it's the exercise that causes the weight loss, because the 162 00:11:54,084 --> 00:11:57,700 exercise occurs before the weight loss occurs. 163 00:11:57,700 --> 00:12:02,370 We can use that temporal relation to decide what causes what. 164 00:12:02,370 --> 00:12:07,543 At least in some cases, because sometimes when you're dealing with a constant 165 00:12:07,543 --> 00:12:12,783 factor, like CO2 variations or pollution variations over a long period of time. 166 00:12:12,783 --> 00:12:18,225 If pollution causes acid rain you don't know exactly which part of the pollution 167 00:12:18,225 --> 00:12:23,533 is causing which part of the acid rain. And the parts are occurring over a long 168 00:12:23,533 --> 00:12:26,959 period of time so you can't tell which comes first. 169 00:12:26,959 --> 00:12:32,267 But at least in cases where you can, like the exercise and the weight loss, then 170 00:12:32,267 --> 00:12:37,140 you can say which causes which. Now when they're not in temporal order 171 00:12:37,140 --> 00:12:40,620 like that, you have to use a different method. 172 00:12:40,620 --> 00:12:44,717 The second method of determining what causes what, is manipulation. 173 00:12:44,717 --> 00:12:48,815 This method is used in a lot of scientific experiments where they 174 00:12:48,815 --> 00:12:53,224 manipulate one factor to see whether that factor causes something else. 175 00:12:53,224 --> 00:12:58,191 When you do those types of experiments you have to make a lot of assumptions or 176 00:12:58,191 --> 00:13:02,972 test a lot of background conditions to make sure that there's no independent 177 00:13:02,972 --> 00:13:05,270 factor that's causing the effect. But. 178 00:13:05,270 --> 00:13:10,296 In the right circumstances it can work. If you want more detail about which 179 00:13:10,296 --> 00:13:15,188 circumstances need to be met, then they're books by Woodward and by Pearl 180 00:13:15,188 --> 00:13:20,147 that spell this out in great detail. But here we're not going to go in any 181 00:13:20,147 --> 00:13:23,632 great detail. We're just going to give you the basic 182 00:13:23,632 --> 00:13:26,782 idea. And the basic idea is that you manipulate 183 00:13:26,782 --> 00:13:31,473 A and then look to see whether B changes, according to the change in A. 184 00:13:31,473 --> 00:13:37,035 And you manipulate B and then look to see whether A changes, in accordance with the 185 00:13:37,035 --> 00:13:40,826 change in B. And that's going to help you determine 186 00:13:40,826 --> 00:13:45,140 what causes what. Because if when you manipulate A, B 187 00:13:45,140 --> 00:13:49,288 changes, then that's an indication that A causes B. 188 00:13:49,288 --> 00:13:55,178 Because if B caused A, then manipulating A wouldn't have an effect on B. 189 00:13:55,178 --> 00:14:01,023 Think about it this way. You put the wood into, the engine, of a 190 00:14:01,023 --> 00:14:05,235 steam train. And the wood affects the motion of the 191 00:14:05,235 --> 00:14:08,666 train because it affects the way the engine works. 192 00:14:08,666 --> 00:14:13,811 And then the train produces steam, but the steam doesn't cause the train to 193 00:14:13,811 --> 00:14:18,683 move, it's caused by the train. So if you manipulate the steam, like with 194 00:14:18,683 --> 00:14:23,280 wind blowing the steam around, that's not going to change the train. 195 00:14:23,280 --> 00:14:30,223 So similarly, if you change A, and B changes, but you change B and A doesn't 196 00:14:30,223 --> 00:14:35,853 change, then that's a pretty good indication that A causes B. 197 00:14:35,853 --> 00:14:39,700 And that's going to rule out, the second one. 198 00:14:39,700 --> 00:14:42,770 Right? That can't be true if a causes b. 199 00:14:42,770 --> 00:14:46,786 The third one. That says that c causes them both, so 200 00:14:46,786 --> 00:14:50,250 it's going to rule that out. And the fourth one. 201 00:14:50,250 --> 00:14:54,502 It's not just an accidental correlation if a causes b. 202 00:14:54,502 --> 00:15:00,880 So if you do that manipulation, you can find out that it really is the first case 203 00:15:00,880 --> 00:15:05,210 that holds. At least, if all the other conditions are 204 00:15:05,210 --> 00:15:08,438 met. Now, you can also do it the other way 205 00:15:08,438 --> 00:15:13,280 around. If you manipulate b, and a changes. 206 00:15:13,280 --> 00:15:21,700 But you manipulate A and B doesn't change, then you know that B causes A. 207 00:15:21,700 --> 00:15:25,920 But. You also know. 208 00:15:25,920 --> 00:15:31,628 That a does not cause b. Because if it did, the new manipulated AB 209 00:15:31,628 --> 00:15:35,492 would change. And you know that, it's not just some 210 00:15:35,492 --> 00:15:39,742 third cause, and you know that it's not just accidental. 211 00:15:39,742 --> 00:15:45,072 So now you've isolated the second. Condition, as the one that holds, if when 212 00:15:45,072 --> 00:15:49,801 you manipulate B, A changes, but when you manipulate A, B doesn't change. 213 00:15:49,801 --> 00:15:53,990 Again this only holds when certain circumstances are in place. 214 00:15:53,990 --> 00:15:59,665 and I'm not going to go into detail and spell out what those are, but this is the 215 00:15:59,665 --> 00:16:03,043 basic idea behind a lot of experiments in science. 216 00:16:03,043 --> 00:16:07,915 Here's an example. Long time ago they discovered that 217 00:16:07,915 --> 00:16:14,680 smoking correlated with lung cancer. And that lead a lot of people to say that 218 00:16:14,680 --> 00:16:18,851 smoking causes lung cancer. But just imagine that you're a 219 00:16:18,851 --> 00:16:22,375 manufacturer of cigarettes. What are you going to do? 220 00:16:22,375 --> 00:16:27,265 Well they were pretty inventive. They said, it's not the smoking that 221 00:16:27,265 --> 00:16:31,489 causes the lung cancer. It's the lung cancer that causes the 222 00:16:31,489 --> 00:16:34,962 smoking. Because when people have certain types of 223 00:16:34,962 --> 00:16:40,240 incipient lung cancer, that is lung cancer that's about to develop into full 224 00:16:40,240 --> 00:16:43,434 blown lung cancer, it makes them want to smoke. 225 00:16:43,434 --> 00:16:48,296 It creates a discomfort in their lungs that's relieved by the smoking. 226 00:16:48,296 --> 00:16:53,500 And that explains why people who smoke more have more lung cancer. 227 00:16:53,500 --> 00:16:58,383 So, we think that A causes B, the smoking causes the lung cancer. 228 00:16:58,383 --> 00:17:03,499 Their claiming that B causes A, the lung cancer causes the smoking. 229 00:17:03,499 --> 00:17:08,249 How do you tell. Well you can't look at the temporal 230 00:17:08,249 --> 00:17:13,515 relation, because the smoking is going on at the same time when the lung cancers 231 00:17:13,515 --> 00:17:16,580 developing. So you got to do a manipulation. 232 00:17:16,580 --> 00:17:21,797 You set it up in a lab. You take a bunch of animals, in this case 233 00:17:21,797 --> 00:17:24,730 it was monkeys. Who don't have lung cancer. 234 00:17:24,730 --> 00:17:28,087 You check 'em first, and then you force them to smoke. 235 00:17:28,087 --> 00:17:32,775 You put a cigarette in their mouth so that when they're breathing they're 236 00:17:32,775 --> 00:17:35,816 smoking. And sure enough they developed a lot of 237 00:17:35,816 --> 00:17:38,540 lung cancer, no surprise there, poor animals. 238 00:17:38,540 --> 00:17:43,830 And that showed that it was lung cancer that was caused by smoking instead of 239 00:17:43,830 --> 00:17:45,187 causing smoking. Why? 240 00:17:45,187 --> 00:17:48,239 Because we didn't manipulate the lung cancer. 241 00:17:48,239 --> 00:17:53,530 It didn't affect the smoking but we manipulated the smoking and that affected 242 00:17:53,530 --> 00:17:58,685 the lung cancer and that shows that it was the smoking that caused the lung 243 00:17:58,685 --> 00:18:02,580 cancer. Here's another example, which is kind of 244 00:18:02,580 --> 00:18:07,920 a mystery for awhile, an economist friend of mine told me about this. 245 00:18:07,920 --> 00:18:14,124 Turns out back in the 1960's there was a positive correlation between having a 246 00:18:14,124 --> 00:18:17,971 television in the home and performance in school. 247 00:18:17,971 --> 00:18:24,253 Students were more successful in school if they had a television in their house. 248 00:18:24,253 --> 00:18:29,593 Some people actually claimed that television was making them better 249 00:18:29,593 --> 00:18:33,532 students. But in the 90's it was the other way 250 00:18:33,532 --> 00:18:37,028 around. In the 90's kids who had televisions in 251 00:18:37,028 --> 00:18:43,053 their home did not do as well on average as kids who had no televisions in their 252 00:18:43,053 --> 00:18:46,102 home. So, there was a negative correlation. 253 00:18:46,102 --> 00:18:51,681 So how could there be a positive correlation between television and school 254 00:18:51,681 --> 00:18:57,557 success in the 1960's and a negative correlation between television and school 255 00:18:57,557 --> 00:19:02,960 sucess in the 1990's? In order to decide between those 256 00:19:02,960 --> 00:19:07,796 hypotheses, you can't manipulate it. You can't go back and change the rate at 257 00:19:07,796 --> 00:19:12,697 which they're watching television and change their school performance and so 258 00:19:12,697 --> 00:19:14,988 on. You need to have some background 259 00:19:14,988 --> 00:19:19,761 information about the societies of the time and the kind of people who had 260 00:19:19,761 --> 00:19:23,007 televisions. Well back in the 1960's it was largely 261 00:19:23,007 --> 00:19:28,162 the more affluent, higher socioeconomic status people who had televisions because 262 00:19:28,162 --> 00:19:31,090 they were quite expensive and not that common. 263 00:19:31,090 --> 00:19:37,216 And those people typically are correlated with high school success anyway. 264 00:19:37,216 --> 00:19:43,010 High socioeconomic status and high school success had been correlated. 265 00:19:43,010 --> 00:19:49,385 And so, it's that correlation that's explaining why in the 1960s students who 266 00:19:49,385 --> 00:19:53,690 had televisions in their homes did better in school. 267 00:19:53,690 --> 00:19:56,985 But in the 1990s things have turned around. 268 00:19:56,985 --> 00:20:02,427 Parents were worried about their children watching too much television. 269 00:20:02,427 --> 00:20:06,692 And some parents. Actually kept televisions out of the home 270 00:20:06,692 --> 00:20:11,770 so we the kids would read more. They read more books and less television. 271 00:20:11,770 --> 00:20:15,864 And those kids were doing better in school, no surprise there. 272 00:20:15,864 --> 00:20:20,832 So in the 1990's there were a lot of people who kept televisions out, that 273 00:20:20,832 --> 00:20:26,001 produced greater school performance. And so you've got different correlations 274 00:20:26,001 --> 00:20:31,438 in different times and the only way to tell what was causing what was by looking 275 00:20:31,438 --> 00:20:36,607 at the background circumstances and knowing something about the societies in 276 00:20:36,607 --> 00:20:41,765 which these effects are occurring. There's a lot more to say about causation 277 00:20:41,765 --> 00:20:46,489 and we can't say it all here. You really ought to take a whole course 278 00:20:46,489 --> 00:20:49,639 about causation and it's many different forms. 279 00:20:49,639 --> 00:20:53,747 But we can help you avoid some of the most common fallacies. 280 00:20:53,747 --> 00:20:59,019 Here we'll look at two common fallacies. The first one is simply confusing an 281 00:20:59,019 --> 00:21:02,520 accidental correlation with a causa relation. 282 00:21:02,520 --> 00:21:08,458 Sometimes this is called post hoc ergo propter hoc, or some people pronounce it 283 00:21:08,458 --> 00:21:13,495 post hoc ergo propter hoc. And what it means in Latin is after this 284 00:21:13,495 --> 00:21:18,457 therefore because of this. And the idea is that they're correlated, 285 00:21:18,457 --> 00:21:23,719 one occurs after the other, and you conclude the second one must occur 286 00:21:23,719 --> 00:21:27,929 because of the first one. It's really just an accidental 287 00:21:27,929 --> 00:21:33,267 correlation, and it's a fallacy to conclude that it's a causal relation. 288 00:21:33,267 --> 00:21:36,500 And of course the classic example occurred. 289 00:21:36,500 --> 00:21:41,042 One time in a hotel with my son, we got onto the elevator and I reached for the 290 00:21:41,042 --> 00:21:43,745 button. I wanted to push for the button for the 291 00:21:43,745 --> 00:21:47,770 floor that we wanted to go to, but my son said, no daddy, no don't do it. 292 00:21:47,770 --> 00:21:53,895 I said okay, and then he reached up. And pushed the button and just as he 293 00:21:53,895 --> 00:21:56,840 pushed the button, the fire alarm went off. 294 00:21:56,840 --> 00:22:01,329 And he started crying because he had made the fire alarm go off. 295 00:22:01,329 --> 00:22:06,800 But of course, he was just committing the fallacy of post hoc ergo propter hoc. 296 00:22:06,800 --> 00:22:10,657 The fire alarm didn't go off because he pushed the elevator button. 297 00:22:10,657 --> 00:22:14,457 It just happened to go off after he pushed it, the elevator button. 298 00:22:14,457 --> 00:22:19,120 Now that might seem like a silly mistake that only a child would make but you'll 299 00:22:19,120 --> 00:22:23,495 be surprised how many times you can find this mistake being made, in serious 300 00:22:23,495 --> 00:22:26,980 discussions in newspapers. Just take a look for yourself. 301 00:22:26,980 --> 00:22:34,238 Now the second common fallacy that I want to describe is confusing a cause with an 302 00:22:34,238 --> 00:22:36,860 effect. Here's some example. 303 00:22:36,860 --> 00:22:41,835 I'm a golfer. And sometimes, I go out and play golf and 304 00:22:41,835 --> 00:22:46,775 my back is just killing me. And I'm thinking, it must be why I'm 305 00:22:46,775 --> 00:22:49,598 playing so bad. I can't hit the ball. 306 00:22:49,598 --> 00:22:54,381 I keep knocking it off to the side. And I blame it on my back. 307 00:22:54,381 --> 00:22:59,400 So I think it's the pain in my back that's causing my bad swing. 308 00:22:59,400 --> 00:23:04,157 And actually, it's my bad swing that's causing the pain in my back. 309 00:23:04,157 --> 00:23:09,131 I'm like twisting around the wrong way and that's causing a backache. 310 00:23:09,131 --> 00:23:14,537 So I think that it's the pain in my back that's making me swing badly, it's 311 00:23:14,537 --> 00:23:18,790 actually the bad swing that's causing the pain in the back. 312 00:23:18,790 --> 00:23:24,662 So that's just an everyday example. Here's an example in football, American 313 00:23:24,662 --> 00:23:29,804 football we're talking about here. It was noticed that there was a 314 00:23:29,804 --> 00:23:34,784 coloration between the number of forward passes, and how often the team was 315 00:23:34,784 --> 00:23:37,839 losing. Teams that had a lot of forward passes 316 00:23:37,839 --> 00:23:42,022 were losing more often. So some coaches concluded, well we ought 317 00:23:42,022 --> 00:23:46,870 not to pass the ball so much because passing the ball causes you to lose. 318 00:23:46,870 --> 00:23:51,091 That's not what was going on. What was going on, is that, in the last 319 00:23:51,091 --> 00:23:55,186 quarter of the game, the team that was behind would get desperate. 320 00:23:55,186 --> 00:23:59,785 They needed to score a lot of points quickly, and running the ball wasn't 321 00:23:59,785 --> 00:24:02,620 going to do that. So they tried passing the ball. 322 00:24:02,620 --> 00:24:06,652 And they would pass more and more and more, in order to catch up. 323 00:24:06,652 --> 00:24:11,629 And that meant was the fact they were losing the game that caused them to pass 324 00:24:11,629 --> 00:24:15,409 the ball so often. It wasn't passing the ball often that was 325 00:24:15,409 --> 00:24:19,819 causing them to lose the game. So again, they were confusing cause with 326 00:24:19,819 --> 00:24:23,160 effect. Here's a third example. 327 00:24:23,160 --> 00:24:29,459 It turns out that many schizophrenics smoke and take drugs and alcohol. 328 00:24:29,459 --> 00:24:36,201 And so, there's a correlation between schizophrenia and drug and alcohol use 329 00:24:36,201 --> 00:24:42,846 including nicotine from smoking. Some people concluded, it must be the 330 00:24:42,846 --> 00:24:48,624 drugs and nicotine cause schizophrenia. No, that's not what's going on. 331 00:24:48,624 --> 00:24:52,030 That's the fallacy of confusing cause and effect. 332 00:24:52,030 --> 00:24:58,563 What's really going on is that their schizophrenia is causing them to smoke 333 00:24:58,563 --> 00:25:01,400 and drink more. And to take drugs. 334 00:25:01,400 --> 00:25:05,782 So again, what people get confused about, is they think that one thing causes 335 00:25:05,782 --> 00:25:09,761 another, when it's really the other thing that causes the first thing. 336 00:25:09,761 --> 00:25:12,645 Their getting the relation backwards and confused. 337 00:25:12,645 --> 00:25:17,143 And that's something that you need to really be on the lookout for as you try 338 00:25:17,143 --> 00:25:20,200 to figure out what causes what in your everyday life. 339 00:25:20,200 --> 00:25:23,956 Because if you make that mistake you're going to get confused. 340 00:25:23,956 --> 00:25:28,223 You're not going to know what manipulations will change what features 341 00:25:28,223 --> 00:25:31,725 in your life. And that's why causal relations report to 342 00:25:31,725 --> 00:25:36,756 figure out how to get around in the world and bring about the changes that you 343 00:25:36,756 --> 00:25:39,621 want. So, watch out for these fallacies and do 344 00:25:39,621 --> 00:25:42,869 the best you can to figure out how the world works.