Contending for the Faith

Margarine and Bad Marriages

Three Angels Broadcasting Network

Program transcript

Participants: Clifford Goldstein


Series Code: CFTF

Program Code: CFTF000006

00:20 Hi, Clifford Goldstein here your host
00:23 for Contending for the Faith and this is part of a series
00:27 I've been doing on faith and science.
00:31 And you know, speaking of science
00:33 I recently came across some fascinating studies
00:38 using the scientific method of correlation.
00:42 And they came to some fascinating conclusions.
00:46 Now remember I'm talking science here.
00:50 So we have to give this serious consideration.
00:53 I mean, if science could get us to the moon,
00:56 build nanotechnology and GPS systems
01:01 that we can carry in our pockets
01:03 then it's got to be on to something, right.
01:06 Well, of course science is on to something
01:08 I don't think there's any question about that.
01:11 Exactly what it's on to well, that's another matter
01:15 and that's what, what exactly is science on to
01:20 when it tells us things about the world.
01:23 Anyway there is a study showing
01:28 a close statistical correlation between the use of margarine
01:34 and the rates of divorce in a home.
01:37 I mean, there is a chart you can follow
01:40 which shows an amazing correlation between the two.
01:45 The use of margarine tracks the weight of divorce
01:50 with the 99% correlation.
01:54 I mean, incredible.
01:56 I mean, it shows up
01:57 when there was more margarine in home the divorce rates go up
02:02 and when there is less margarine
02:04 the divorce rates go down.
02:07 The correlation is astonishing.
02:11 But there is more.
02:13 Did you know that there is a power correlation
02:16 between the rising per capita consumption of cheese
02:22 to the number of people
02:24 who died by coming entangled in their bed sheets.
02:28 I mean, the correlation is there,
02:31 you can't deny the correlation.
02:34 They track each other
02:37 that is the rate of cheese consumption
02:40 and the rate of those who died by being tangled
02:43 in their bed sheets track each other very closely.
02:49 How could that be?
02:51 Who knows and you can earn it better
02:53 all you want but you can't deny
02:55 the reality of the correlation.
02:59 Or what about the amazing correlation
03:03 between the rising number of Facebook users
03:06 and the growing Greek death crises.
03:10 The Correlation is there.
03:13 Or what about the fact
03:14 that there is an incredibly close statistical correlation
03:18 between the number of people
03:20 who drown in swimming pools every year
03:24 and the number of movies that Nicolas Cage appear in.
03:28 I mean, if you were to chart a graph looking
03:31 at the correlation you would be astonished,
03:35 one follows the other as closely
03:37 as heat follows fire.
03:41 Now, of course come on here,
03:45 oh, this is a bit crazy.
03:46 I can't be serious. Can I?
03:49 Well, in one level of course not.
03:52 I got these powerful correlations
03:55 from a website called "Spurious Correlations."
04:00 And the name says it all.
04:02 It was some criminology student
04:04 in Harvard university created the site
04:08 but he was making a serious point.
04:11 He was making the serious point
04:13 that we have to be very careful
04:16 when we hear scientific pronouncements saying that
04:21 a link has been found between X and Y.
04:25 You can fill in the blanks for X and Y,
04:29 you would have a lot of options.
04:33 I mean, after all haven't we been study showing
04:37 that the rise in alcohol consumption
04:40 at least to a certain degree
04:42 leads to a decrease in heart attacks.
04:45 There's been some scientific studies making that claim
04:49 because there been some powerful correlations
04:52 between the two.
04:54 Of course, there had been other scientific studies
04:57 that have challenged that and I guess the question is,
05:01 which one is correct.
05:03 I mean, haven't we also been told for decades
05:07 that an increase in fat consumption
05:10 can lead to heart attacks.
05:12 I mean, the correlation was there,
05:14 no one's gonna deny it.
05:16 But you know, I recently saw a headline
05:19 in Time Magazine with new scientific studies
05:23 saying that that correlation was false.
05:26 The headline in Time Magazine,
05:30 June 12th, 2014 read,
05:33 "Ending the War on Fat" and the caption said,
05:40 "For decades, it has been the most
05:42 vilified nutrient in the American diet.
05:46 But new science reveals fat isn't
05:50 what's hurting our health."
05:52 Whoa. New science?
05:55 That means for decades all the old science was wrong.
05:59 Science wrong, all those powerful correlations
06:04 between fat and heart diseases
06:07 were apparently as misleading or maybe not
06:10 but to some degree is misleading
06:12 as the correlation between the number of movies
06:15 Nick Cage plays in and the number of people
06:18 who drown in swimming pools.
06:21 What about the correlation
06:23 between vaccinations and autism
06:25 or cancer and living near high power wires
06:29 or the correlation between women taking hormone therapy
06:33 and lower than average rates of heart diseases.
06:37 Or how about this correlation?
06:41 The faster windmills are observed to rotate,
06:46 the more wind is observed to be present.
06:49 So the correlation is there.
06:52 The faster moving windmills the more wind is there.
06:56 Thus contra-logically argue that
06:58 the windmills create the wind?
07:01 I mean, they are called windmills, right,
07:04 and you can't deny
07:05 the correlation either, can you?
07:08 Now of course,
07:10 everyone knows or should know
07:13 and that was-- which is the whole point
07:15 of spurious correlation the website
07:18 is that correlation is not causation.
07:22 The two events closely correlate
07:25 such as the drowning in swimming pool
07:27 and Nick Cage the movies Nick Cage starred
07:31 doesn't mean that one causes the other
07:33 or that even one has anything to do with the other.
07:37 Okay, it might and scientist and people
07:41 and every day do they look at correlation,
07:44 try to discover causes but that doesn't mean
07:48 the correlation is the cause.
07:51 And the million dollar question
07:54 of science is how do you determine
07:58 if correlation is or isn't a cause.
08:01 Sometimes it is, some times it isn't
08:05 but how do we know the difference?
08:08 Now the correlation is not causation,
08:11 argument can be a very powerful argument.
08:16 After all as we just saw the correlation
08:19 between margarine and divorce
08:21 and clearly one is not the cause of the other.
08:26 But you know, the tobacco industry
08:29 for decades for as long as it could get away
08:31 with argued against the causation.
08:34 It argued used the causation
08:37 is not correlation argument against the correlation
08:41 between smoking and the diseases
08:44 that are believed to be caused by smoking.
08:48 With this the marriage
08:50 the margarine divorce correlation
08:53 and yet we-- and yet scientist working
08:56 for big tobacco tried to disk
08:58 the cancer cigarette correlation as well.
09:02 I mean, after all George Burns smokes cigars
09:07 and George Burns lived to be 100 years old.
09:12 But anyway the point is correlation is not causation
09:17 except when it is causation.
09:20 And so the big question for science is,
09:22 how do we know?
09:24 How do we know that our theories match
09:26 and explain the facts on the ground?
09:28 How do we know that our understanding,
09:30 our explanations of causes are correct?
09:34 How does scientist know that their data
09:37 and that their interpretation of the data is right?
09:41 You know, I recently saw something on Twitter.
09:44 It showed a flood in Saudi Arabia.
09:49 A flood in Saudi Arabia, I mean, come on.
09:53 If that is it more proof of global warming what is?
09:58 I mean, if that doesn't clinch the argument
10:01 for global warming what does?
10:03 Talk about proof, the only problem,
10:06 the only problem the flood occurred
10:09 in Saudi Arabia in 1941 long before
10:14 we were supposedly destroying
10:16 the world with our carbon footprint.
10:18 Now here's the point here.
10:20 I don't want to get into this global warming
10:23 debate one way or the other.
10:25 Only I do find it very spurious, very silly
10:31 that every time there is an exceedingly hot day
10:34 or there is a new storm,
10:35 or there is some extreme in weather
10:38 we are sure by the people who know this more proof,
10:42 this is more evidence of global warming.
10:46 Now if you been-- maybe they are
10:48 maybe they aren't
10:50 but the fact proving things in science
10:53 is despite all of the hype to the contrary
10:56 a very problematic, very, very subjective exercise.
11:02 See, if you've been following this program,
11:05 if you've been following this series at all
11:07 you will see, you will have seen that
11:09 science is not this clear cut objective pursuit of truth
11:15 that it's often been portrayed as.
11:18 On the contrary almost from the time science
11:21 or what was called natural philosophy was practiced.
11:25 Questions has arisen about just what it does?
11:29 How it does, what it does?
11:31 And what does it tell us?
11:32 And how much of it can we really trust?
11:36 You know, as you know here we are,
11:38 we are in the 21st century
11:40 and we are living with many of the great
11:42 and sometimes not so great technological fruits of science
11:47 and the amazing thing is
11:50 these questions still remain unanswered.
11:55 There is nil, there is still no real consensus
11:58 about what true science is
12:01 or what the scientific method entails
12:03 or what science really does teach us about the world.
12:09 And I want to give you one important example
12:11 of the limitations of what science tells us.
12:15 A limitations that scientist have been
12:17 aware of for centuries that men like Francis Bacon
12:21 and Rene Descartes and Isaac Newton
12:24 these are men from hundreds of years ago
12:27 were aware of and to do
12:30 so I want to talk about something
12:31 that I talked about in an earlier lecture
12:34 but in an earlier program we're going to whip through it.
12:38 You know, I talked how I was raise
12:40 and educated on evolution.
12:43 Taught it my whole life,
12:45 then one day I became a born again believer in Jesus
12:49 and almost from the start I saw that
12:51 there was a contradiction between what I believed.
12:57 Well, somebody gave me some creation as literature
12:59 and as I said I didn't know
13:01 if the stuff was any good or not.
13:05 But what happened was this,
13:08 as I read it the scales came off my eyes
13:11 because as I said for the first time in my life
13:15 I was open to the idea
13:18 that there was an alternative explanation
13:21 to the facts in the ground.
13:23 As I said no one is going to deny
13:25 the dinosaur bones in the ground.
13:28 But for the first time-- I was 24 years old
13:31 and for the first time I was taught that
13:34 there was another option to explain them
13:37 other then the Darwinian one
13:39 that I had been imbued with,
13:41 that I had been propagandized with that.
13:43 I believe that I had been brain washed with
13:46 since I was a child, okay.
13:50 Now remember some argue that
13:51 science does tell us about the real world,
13:54 others argue that either that it can tell us
13:57 only what things do in the real world.
14:00 And if you have seen one of the earlier shows
14:03 some tell us it can all science can do is tell us
14:06 how the world appears to our senses, okay.
14:12 But the one thing
14:13 but with the idea that science--
14:15 let's just go with the idea for a minute,
14:18 that science can tell us about the real world
14:22 and about why things happen in the real world.
14:25 The scientific project faces a great challenge.
14:30 It's been what has been called
14:32 the under determination of theories by evidence.
14:38 Now, let's think about this for a minute,
14:42 suppose I propose a scientific theory
14:47 and I claim that if my theory is correct
14:52 "Y" is going to turn red for such and such reasons.
14:58 Well, what do you know?
15:00 Y turns red just as my theory predicted
15:06 and even more impressively
15:08 suppose Y turns red 100% of the time.
15:16 Every time I do my predict-- every time I have my theory
15:20 and I do my experiment
15:22 and I say Y is going to turn red
15:24 because of blah, blah, blah, and Y turns red.
15:27 And that say I do my experiment at night,
15:30 I do it in hot weather, I do it in cold weather,
15:33 I do it in a submarine, I do it in an airplane.
15:37 Every time I do my experiment
15:41 Y turns red 100% of the time.
15:47 Well, then what can you conclude
15:50 other than that my theory is correct,
15:53 if predicted Y would turn red
15:56 and Y turned red every time too.
16:00 Thus what can you conclude
16:03 than other than that my theory is right?
16:07 Right?
16:09 Well, no, not necessarily, not necessarily at all.
16:16 In fact, white could turn red
16:18 for reasons that have nothing to do with my theory.
16:23 Other theories, other explanations
16:25 could make the exact predictions that mine did.
16:29 A completely different theory
16:32 with completely different promises,
16:34 completely different understanding of reality
16:38 and yet it comes up with the same thing
16:42 every time I do X, Y turns red.
16:48 In fact, you know, you often taught well,
16:50 your science makes a prediction,
16:51 he comes up with a theory, he makes a prediction,
16:53 the prediction turns out right, the theory is true.
16:56 This is what some people call the scientific method
17:01 but you know, some people argue
17:04 that correct predictions never ever prove a theory right.
17:10 All they show is that has yet to be falsified.
17:13 Remember if you saw on earlier show
17:16 I talked about the Ptolemy view of the universe.
17:19 This is the idea that the earth sits stationary.
17:24 The center of the universe
17:26 and the planets and the stars and everything circle there.
17:29 People believe that for a thousand years
17:31 and yet as I said on the show
17:33 you couldn't make accurate predictions.
17:36 You could predict what the stars were going to do,
17:39 you could sail your ships where you want to do and it worked.
17:43 But you know, we now know even though it worked,
17:47 even though it made accurate predictions
17:50 we now know the theory was wrong, completely wrong.
17:55 The foundations of it were completely off.
18:01 So this idea then that just because
18:04 you can make a prediction,
18:06 you build a theory and make predictions
18:09 and the predictions work is no guarantee
18:13 that the theory is successful, okay.
18:16 It might be right, you might be right
18:19 and we might have great reasons
18:21 other than the mere accuracy of the predictions
18:24 to believe that the theory is right.
18:27 But to believe that its right
18:29 just because of these predictions
18:31 is to make what's called a varied--
18:33 to make a logical fallacy.
18:37 Let's do for a moment a tiny little bit of former logic.
18:41 Now just listen carefully,
18:43 listen carefully to what I'm going to say.
18:46 If we say that A is true then B must be true as well.
18:52 It doesn't matter what A and B are,
18:54 that's the point of former logic.
18:56 They are not interested in specifics.
18:58 All former logic wants to do is deal with relationships
19:03 between specifics, that's all.
19:05 So you got A and you B doesn't matter what they are.
19:09 So we say if A is true then B is true okay,
19:14 and what do you know, A is true,
19:17 thus logically B has to be true as well.
19:21 You say, if you got A and you got B
19:23 and if A is true than B is true.
19:26 A is true therefore B has to be true.
19:30 It's simple logic it's called the fancy term
19:34 is modus ponens, modus ponens.
19:38 Now listen to this,
19:40 this is subtle but pick it up because it's important.
19:43 However to say that if A is true then B is true
19:50 and that B is true
19:53 doesn't necessarily mean that A is true.
19:57 B can be true for reasons that have nothing to do with A.
20:01 Let me give you an example.
20:03 If every-- if we have A
20:05 I put on my hat on my head and B it rains.
20:10 It doesn't mean that suddenly B if it's raining
20:13 that I have to put my hat on my head, okay.
20:17 The rain could have absolutely nothing to do with me
20:20 putting my hat on my head.
20:22 It's probably have something to do
20:23 with the low pressure system
20:26 that moves into the area, okay.
20:30 And yet don't miss this crucial point
20:35 and this point is that almost all,
20:37 I would say all or almost all modern science
20:41 and the practice of science
20:43 is built on this kind of experimental prediction.
20:48 You look at something or you do an experiment
20:51 and then you-- based on your understanding
20:54 of what is happening based on your theory
20:57 you make the predictions
20:59 and indeed if those predictions come true
21:02 you claim your theory is right.
21:05 Let me give you a quick example.
21:07 I have a theory, I have a theory
21:11 that they are invisible spiders from Mars
21:16 and these spiders from Mars are pushing everything down.
21:20 Now I want you to test it with me.
21:22 I want you to-- I'm going to raise my hand
21:24 and I let my hand go and boom it drops, okay.
21:28 Do it again. I want to try with this hand.
21:30 You will try it at home,
21:32 you'll lift your hand up and you let it go.
21:34 Does it fall to the ground?
21:37 Well, therefore I just proved my theory.
21:40 Invisible spiders from Mars are pushing everything down.
21:44 Okay, that might be silly but that is reveals
21:49 the fundamental fallacy of every scientific theory.
21:55 It might be right,
21:56 the theory that made the predictions might be right
21:59 but it might not be.
22:01 Your predictions no matter how accurate they are
22:05 might be the result of something entirely different
22:08 from what you propose in your theory, okay.
22:11 That's what this means there.
22:14 What this means that is that
22:15 there is always going to be a limitation on how true
22:20 one can be in regard to what is valid in science.
22:25 Accurate predictions do not make a theory true.
22:30 As we saw you can make some very accurate predictions
22:33 with a theory of the universe
22:35 that had a stationary earth at the beginning of it.
22:38 A stationary earth with the sun and stars moving around it.
22:43 They made accurate predictions totally wrong theory
22:48 and this is not as uncommon as one would think.
22:51 Some times that might even be more of the exception
22:53 rather than the rule.
22:55 I talked to you in an earlier program
22:57 about general relativity and quantum theory.
23:01 Two theories that have been proven beyond
23:04 and they make incredibly accurate predictions,
23:07 it's amazingly accurate predictions
23:09 and yet we saw that
23:11 they basically contradict each other.
23:13 Scientist know that quantum theory
23:15 as we understand it and general relativity
23:18 as we understand it in certain ways
23:20 and complete contradiction to each other.
23:24 But how could that be?
23:25 They make accurate predictions.
23:28 Well, the answer is easy,
23:29 accurate predictions doesn't necessarily mean
23:33 that a theory is true.
23:36 I talked to you also about how?
23:39 In the past you could have theories that
23:40 they built technology on.
23:43 Technology was built on theories
23:45 that have been proven wrong.
23:48 I got this quote from a professor
23:50 of philosophy named Dr. Goldman.
23:53 "The theories we currently hold to be true
23:56 are as likely to be falsified in the next 100 years
24:00 as the theories that we look back on
24:02 as having been falsified in the last hundred years.
24:07 Wow, that's pretty heavy.
24:09 So the theories we hold today,
24:11 theories that many ways work
24:14 could later be shown to be false
24:16 as were many theories in the past
24:18 that were believed that worked,
24:21 that made predictions were shown to be false.
24:26 You know, I'm not going to do it now
24:30 but I think it would be fun.
24:31 It would be fun to do a whole program,
24:34 do a whole program on scientific theories
24:38 that were believed have been true
24:40 and that they built technology on fruitful technology
24:45 that later were shown to be false.
24:48 But I can think for a few ideas
24:50 that were once so believed in science
24:54 and that now had been rejected.
24:56 The existence of the universe of an eternal universe
25:00 once a foundation theory of science is gone,
25:06 known to be, believed to be wrong now.
25:08 The static nature of the universe,
25:11 the universe was once believed to be static, gone.
25:15 The idea of absolute space,
25:17 absolute time the two assumptions that
25:20 Newton used to create his theory of gravity, gone.
25:25 And a big one,
25:26 the idea that energy was continuous
25:29 that it could be divided-- infinitely divided in smaller,
25:33 smaller sections on and on and on.
25:36 This is the foundation of quantum theory
25:39 and it's completely gone.
25:41 If you are seeking truth as in the truth
25:45 then science is probably going to directly disappoint you.
25:50 On the other hand if you want as we said,
25:53 if you are just looking to build a better mousetrap,
25:56 if you are just looking to build some technology
25:59 then you don't care about these questions.
26:03 In fact, one of the most famous philosophers
26:05 of science Karl Popper has argued this very point.
26:10 He said we can never prove a scientific theory true.
26:15 All we can do is show that it is false.
26:20 We can never prove a theory true
26:22 only that it hasn't been proven false.
26:25 What does that mean?
26:27 Well, Popper still very influential
26:30 but he basically to this conclusion
26:32 from the same reason about what we are saying.
26:34 You could have all the predictions you want,
26:38 you can make all the proper predictions
26:41 based on your theory, you could build weapons,
26:44 you could make money, you can do all these things
26:48 but it doesn't make it true, accurate predictions are cheap.
26:52 Things that-- astrology can make accurate predictions.
26:56 And said for Popper it doesn't matter
27:00 any real scientific theory in principle is falsifiable.
27:06 You can never be sure something new isn't going to come along
27:11 and totally uproot your theory
27:15 regardless of how well of all predictions it makes.
27:21 Now the reason I bring this up again
27:25 though this element comes in as we've looked at is again--
27:28 it just a deal with this idea
27:32 of the certainty of scientific knowledge.
27:36 We-- scientific knowledge is not certain.
27:40 I mean, no knowledge in that sense is certain.
27:43 There are questions, there are loopholes,
27:46 there are things that you have to take on faith on science
27:50 as you do with every thing else.
27:53 Thus science is great, teaches us a lot
27:56 but it's not the final orbiter of truth.


Revised 2015-02-19