Browse content similar to Nate Silver - Statistician. Check below for episodes and series from the same categories and more!
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world's busiest ports. You're watching BBC News. Now, it's time | :00:02. | :00:12. | |
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for Hardtalk. Welcome to HARDtalk. He is seen as a political | :00:25. | :00:28. | |
astrologer with an uncanny ability to get it right. His prediction of | :00:29. | :00:31. | |
the US presidential election result last year was spot on when most | :00:31. | :00:35. | |
pundits were saying the race was too close to call. No wonder then | :00:35. | :00:38. | |
that Nate Silver is being credited with re-shaping the art and science | :00:38. | :00:43. | |
of political forecasting. But has he robbed electoral campaigns of | :00:43. | :00:45. | |
their substance by reducing them to mere statistics and number- | :00:45. | :00:55. | |
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Nate Silver welcome. When it comes to elections, what is the point of | :01:24. | :01:31. | |
prediction. Why not wait and see why people vote? People like to | :01:31. | :01:37. | |
prepare for the future. The word for custom means planning under | :01:37. | :01:41. | |
conditions of uncertainty. Trying to plan who the next president | :01:41. | :01:47. | |
might be, for your personal life or for investing. Good information | :01:47. | :01:51. | |
instead of the bad information you may get from the media. Even if it | :01:51. | :01:57. | |
is a prediction as late as before an election result, what is some | :01:57. | :02:03. | |
point of that? People cannot really make plans. There is a lot of horse | :02:03. | :02:08. | |
race coverage in the States. Some people follow it like they may | :02:08. | :02:13. | |
follow sport. The coverage is often poor when exaggerate how dynamic | :02:13. | :02:17. | |
the horse race is. They claim the candidate behind welcome back and | :02:17. | :02:21. | |
often they will not. They are not looking what the voters are telling | :02:21. | :02:27. | |
them. They are superimposing the pundits's viewpoint on the | :02:27. | :02:31. | |
information. We try to do this and we want to be accurate and truthful | :02:31. | :02:38. | |
and strive to be more scientific with projections. Your predictions | :02:38. | :02:42. | |
consist of three parts, dynamic modelling, data analysis and human | :02:42. | :02:46. | |
judgement. Most people would say you have proved yourself on the | :02:46. | :02:52. | |
first two. How good is your human judgement? She would have to talk | :02:52. | :02:56. | |
to my friends and partners. What you think about your human | :02:56. | :03:03. | |
judgement, due are famous for these computer models? A lot of people | :03:03. | :03:08. | |
are very enamoured with this idea you can make a prediction based on | :03:08. | :03:11. | |
your gut feeling. The evidence shows and a lot of feelings that | :03:11. | :03:17. | |
gut feelings are not very accurate most of the time. Special when it | :03:17. | :03:26. | |
comes to estimating probabilities, we do not really know how the maths | :03:26. | :03:31. | |
works very well. Elgar often leads us astray. You do except it human | :03:31. | :03:35. | |
judgement forms an important part of forecasting and predictions. It | :03:35. | :03:44. | |
is messy? Has people analyse more different factors, it is messy. | :03:44. | :03:48. | |
Whether that judgement, especially if it comes from a pundit that is | :03:48. | :03:53. | |
out of touch with what voters think, and they are going to cocktail | :03:53. | :03:58. | |
parties at Georgetown and serving other A-League friends, the chatter | :03:58. | :04:03. | |
they here, may not be very useful. But if they hear from the campaigns | :04:03. | :04:07. | |
noise and to listen to put that as providing valuable insight and | :04:07. | :04:11. | |
information. Sometimes, sticking with simple things like poles that | :04:11. | :04:15. | |
give us the voters the chance to express themselves to wreck there | :04:15. | :04:22. | |
is a better approach. You process statistics and make models and | :04:22. | :04:26. | |
predictions, when you hear what somebody li?I ? somebody liitish | :04:26. | :04:30. | |
pollsters said he found that you guv, he applauds you and says you | :04:31. | :04:37. | |
are brilliant at taking a lot of data, understanding it, he says | :04:37. | :04:41. | |
without opinion polls and surveys they would be no Nate Silver. You | :04:41. | :04:46. | |
did well because the polls did well. If you have taken every just of | :04:46. | :04:49. | |
those polls you would have reached the same conclusion? I could have | :04:49. | :04:55. | |
done that. Anybody could. But you get all this attention? They is | :04:55. | :05:00. | |
because in politics the one-eyed man is the king in the land of the | :05:00. | :05:05. | |
blind. The coverage is so poor a lot of the time. You have to sell | :05:05. | :05:08. | |
newspapers and television programmes to say it will be a | :05:08. | :05:15. | |
close race. You want to appear unbiased. Obviously, people | :05:15. | :05:19. | |
involved in politics directly were trying to spin a narrative of how | :05:19. | :05:24. | |
the party may win, a lot of inaccurate information. When he | :05:24. | :05:28. | |
sees the did well because the polls were good and you manage to key in | :05:28. | :05:35. | |
is that a key point that anybody could have done it. I said before | :05:35. | :05:41. | |
the election, I would get too much attention of Barack Obama 1 and too | :05:41. | :05:47. | |
much blame if Mitt Romney won. I look at probabilities, there is a | :05:47. | :05:51. | |
margin of error with polls. We look at how accurate polling has been. | :05:51. | :05:55. | |
You have many Poles from many different states which can increase | :05:55. | :06:01. | |
the accuracy. That can influence whether you place a bet. We do not | :06:01. | :06:09. | |
know what is going to happen, it is about Wayne information and data | :06:09. | :06:15. | |
within the context of uncertainty. Did you generate any date yourself? | :06:15. | :06:21. | |
Were each generate data with the models. Do you actually bring in | :06:21. | :06:28. | |
fresh data yourself? I am not a pollster. You're likely lead on the | :06:28. | :06:33. | |
work of opinion polls? The use economic data and a judgement as | :06:33. | :06:40. | |
well in a review of the empirical scientist. I am very widely read. | :06:40. | :06:46. | |
We do not rely on spin from campaigns. We do not rely on | :06:46. | :06:50. | |
conventional wisdom which is not worth much in Washington DC. We | :06:50. | :06:54. | |
rely on poles which reflect what individual people say. Polls that | :06:54. | :06:59. | |
go out and interview action will voters and what they think about | :06:59. | :07:06. | |
the election. As Michael Gerson a prominent member of the Republican | :07:07. | :07:11. | |
establishment, an aide to George bush said, elections are not a | :07:11. | :07:15. | |
mathematical equation? These are the people that thought Mitt Romney | :07:15. | :07:21. | |
would win. That may be the case, there. Is your approach trivialises | :07:21. | :07:27. | |
politics. It's trivialises their ability to spin and confuse people. | :07:27. | :07:37. | |
:07:37. | :07:39. | ||
They have an interest in a site like mine which takes the view from | :07:39. | :07:46. | |
data and best practices. They were proven wrong last November. Michael | :07:46. | :07:54. | |
Gerson makes the point. Why should we be listening to him? He is a | :07:54. | :07:58. | |
senior member of the bush establishment. Why is that a | :07:58. | :08:03. | |
credential? Used to measure public opinion. The measure what people | :08:03. | :08:08. | |
think and how voters may behave. build models based on polling data | :08:08. | :08:16. | |
and economic data. To discuss what forms public opinion. As the | :08:16. | :08:23. | |
election came to a close, we talked to Nate Silver's statistical model, | :08:23. | :08:31. | |
there was not much about social mobility and debt. He said your | :08:31. | :08:36. | |
approach was devoid of substance and trivialises politics. | :08:36. | :08:40. | |
approach is to write a better version of the horse race coverage | :08:40. | :08:47. | |
we see in the United States media every day. It should not be | :08:47. | :08:50. | |
difficult to look at the polls and take an average of them and see who | :08:50. | :08:57. | |
is head. And why do not we trust parties since to talk about the war | :08:57. | :09:02. | |
if they cannot get basic facts right. Adding up one converses the | :09:02. | :09:06. | |
other. I'm not sure why we should listen to sum up like that on | :09:06. | :09:12. | |
complex issues. Because he is a Republican? Because he is wrong. I | :09:12. | :09:16. | |
think he's deluded about what reality is. His biases have | :09:16. | :09:23. | |
overcome his ability to see the polls. I am biased? Are you vised? | :09:23. | :09:28. | |
They everyone is biased. They historian Kim Stanley is accusing | :09:28. | :09:34. | |
you of predicting a win and making him look like a winner, and it is | :09:34. | :09:42. | |
your goal to create, convincing Obama will win because he is a | :09:42. | :09:52. | |
winner. I am telling you what Tim Stanley says. If you take two | :09:52. | :09:54. | |
people are not credible and take the arguments and ignore lots of | :09:54. | :09:59. | |
other people in the field that think highly of my research. It | :09:59. | :10:09. | |
makes for better TV. I was many same that. Looking at data from | :10:09. | :10:13. | |
other interviews to create better ratings for this show what type of | :10:13. | :10:19. | |
journalistic values are those. putting up to that Michael Gerson | :10:19. | :10:25. | |
is being partisan. I think he is wrong. He would say he has a fair | :10:25. | :10:30. | |
point to make. He is saying what you do makes elections devoid of | :10:31. | :10:35. | |
elections by reducing them to a mathematical equation. We have gone | :10:35. | :10:40. | |
through that. Do you worry that even if you if you are not being a | :10:40. | :10:43. | |
vet the partisan that he might be used as a political tall? A thing | :10:43. | :10:48. | |
the media coverage influences the way people can vote and behave. We | :10:48. | :10:54. | |
are trying to use simple models that take poll's as direct measures | :10:54. | :10:58. | |
of public opinion and take them back to the public domain. I do not | :10:58. | :11:03. | |
like the business of politics, I do not live in Washington. The goal is | :11:03. | :11:09. | |
to be to inform the public. Does it worry you that Barack Obama said at | :11:09. | :11:15. | |
a dinner in March this year there are some one very special in my | :11:15. | :11:19. | |
life who is missing who always has my back and stands with me no | :11:19. | :11:24. | |
matter what the matter how dark things seem, my rock, my foundation, | :11:24. | :11:34. | |
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thank you Nate Silver. That was a joke. So he just said it as a joke. | :11:35. | :11:38. | |
That is the number that is the dinner where he makes a number of | :11:38. | :11:45. | |
jokes. His is on the record that you are a democrat. Do you support | :11:46. | :11:55. | |
Barack Obama. I'm am on the centre- left in the US. The idea that | :11:55. | :12:03. | |
people in politics should not have political views is ridiculous. I | :12:03. | :12:07. | |
say that is my subject of a vantage-point, but I'm looking at | :12:07. | :12:09. | |
an object of world we all share. Can I make accurate judgements | :12:09. | :12:15. | |
about that world. By making verifiable predictions and claims | :12:15. | :12:18. | |
that are not the biggest to be inscrutable, you can test on | :12:18. | :12:23. | |
reality. People in the Republican Party for Mitt Romney, came close | :12:23. | :12:27. | |
up to reality when they saw they were looking at the world and a | :12:27. | :12:30. | |
jaded and biased way. You are an interesting phenomenon because you | :12:30. | :12:35. | |
said I try not to talk to campaigns because they are mostly noise. You | :12:35. | :12:40. | |
talk about politics being a game with a lot of and vested interests. | :12:40. | :12:45. | |
ECGD not like to get into political debate and live in Washington. You | :12:45. | :12:50. | |
cannot remain above the fray? think most journalists would not | :12:50. | :12:57. | |
like to be the story. Part of the reason is that my block became | :12:57. | :13:01. | |
symbolic of the polls. When you become invested with symbolic power | :13:01. | :13:07. | |
it is not always your choice. It is a distraction. There are other | :13:07. | :13:12. | |
sites that take a similar approach to what they do. They had largely | :13:12. | :13:19. | |
similar results. All of them by election day, had Barack Obama | :13:19. | :13:22. | |
ahead in the electoral college. When you have any type of signs or | :13:22. | :13:26. | |
academic inquiry we have consensus view., different assumptions that | :13:26. | :13:31. | |
you the same result, that is richer than when your model is based on | :13:31. | :13:37. | |
one parameter. That might be changed. You can no longer remain | :13:37. | :13:42. | |
above the political fray? I think I can. I do not really care about | :13:43. | :13:47. | |
politics. There are things I worry about sports and economics. I have | :13:47. | :13:54. | |
other ways to make a living. I have political views but as compared to | :13:54. | :14:00. | |
people most people, I am a more detached from that world. The study | :14:00. | :14:04. | |
politics at the University of Chicago, then you joined KPMG, you | :14:04. | :14:08. | |
talk to online gambling and made $0.5 million from that. You said | :14:08. | :14:13. | |
Poker gave you better training than anything I can think of about how | :14:13. | :14:18. | |
to weigh new information and what may be less important information. | :14:18. | :14:24. | |
The went into looking at baseball and looking at how players too. His | :14:24. | :14:34. | |
:14:34. | :14:50. | ||
politics another game like poker or You will get lucky sometimes as | :14:50. | :14:56. | |
well. What is the best decision to make under an environment of | :14:56. | :15:01. | |
uncertainty. That perspective is helpful. Knowing what we can and | :15:01. | :15:08. | |
cannot control. Light politics where people take hyperbolic fuse. | :15:08. | :15:18. | |
:15:18. | :15:18. | ||
Manipulating public opinion. -- the views. You want $0.5 million. Why | :15:18. | :15:26. | |
did you stop? That was at the peak. We lost some money after that. | :15:26. | :15:32. | |
Poker was a bubble economy. In the meagre 2000, it was being on | :15:32. | :15:36. | |
television. A lot of players who were not very good were depositing | :15:36. | :15:42. | |
money. I took their money. Eventually the players got better | :15:42. | :15:48. | |
off the bat once gave up all went broke. Anyway, you say the | :15:48. | :15:53. | |
principles for piker, baseball, games and politics all were. Can | :15:53. | :16:00. | |
your model predict any event? Could it have predicted the Arab spring | :16:00. | :16:06. | |
for instance? We look at individual feels. Where there is some degree | :16:06. | :16:11. | |
of predictability. Especially when the competition is not handling it | :16:11. | :16:18. | |
in a very intelligent way. Sports, in the US, ten years ago baseball | :16:18. | :16:23. | |
teams were not very sophisticated. Looking for cases like that. | :16:23. | :16:27. | |
Predicting global and international events is something of a fool's | :16:27. | :16:32. | |
errand. The best that democracies can do is prepare themselves | :16:32. | :16:42. | |
:16:42. | :16:42. | ||
overwriting of outcomes. Your success depends on... In the detail | :16:43. | :16:47. | |
modelling Euan Dunn, worked on the US because there is so much data | :16:47. | :16:57. | |
:16:57. | :16:57. | ||
available. -- you haven't done. would be much more cautious about | :16:57. | :17:01. | |
making predictions about because in Egypt. People may not be | :17:01. | :17:07. | |
comfortable telling their honest opinion. In some countries, where | :17:07. | :17:12. | |
the votes are counted fairly. what about the financial crisis for | :17:12. | :17:17. | |
which there is a great deal of data and there was - could that have | :17:17. | :17:21. | |
been predicted? There were a lot of people who understood the housing | :17:21. | :17:29. | |
bubble well in advance. I should stay that the housing market was | :17:29. | :17:33. | |
the relevant in the room. Seen the prices go up in the US and Western | :17:33. | :17:43. | |
Europe, in the way that you always had a crash to follow. That was an | :17:43. | :17:46. | |
obvious truth. One of the lessons of the book is that you can build | :17:46. | :17:52. | |
are these very complicated and fancy models but if you lack of | :17:52. | :17:56. | |
judgement it still lead you astray. You're talking about the book, the | :17:56. | :18:03. | |
signal and the noise. Amaze all that noise and data, all that data | :18:03. | :18:09. | |
we are overwhelmed with, looking at the financial crisis, do you thank | :18:09. | :18:15. | |
you would be able to, however you do it, to say yes, the euro will | :18:16. | :18:21. | |
survive or not it will crash? of the findings of the book is that | :18:21. | :18:25. | |
the more sure of themselves the pound it is the less likely the | :18:25. | :18:30. | |
outcome. I do not have a strong opinion on whether the eurozone | :18:30. | :18:35. | |
will remain intact or not. I think it teaches us lessons | :18:35. | :18:40. | |
retrospectively about maybe what happens with unknown unknowns. We | :18:40. | :18:43. | |
take a big complex system that is working more less well and you | :18:44. | :18:50. | |
change it, you can anticipate a problem like a common currency but | :18:50. | :18:53. | |
the centralised control of public finances that will create | :18:53. | :18:59. | |
difficulties. I do not think people anticipated how that would player. | :18:59. | :19:04. | |
How funny you predict about how something is going to happen - the | :19:04. | :19:10. | |
British elections in 2015 - the data is too premature? Politics and | :19:10. | :19:14. | |
economics are closely tied together. Economists have been unable to | :19:14. | :19:18. | |
predict recessions or recoveries more than six months in advance. To | :19:18. | :19:23. | |
know the state of British economy in 2015 you would know more about | :19:23. | :19:28. | |
how the politics would turn out all the elections were turnout. What | :19:28. | :19:31. | |
about climate change signs which may spread dish -- predictions | :19:31. | :19:36. | |
about what is going to happen in 70 years' time? Are you saying that is | :19:36. | :19:44. | |
really based close? The reason why in the climate signed predictions | :19:44. | :19:49. | |
but Test worthy is that they are based on theory which has been | :19:50. | :19:54. | |
tested on the bases of relationships that have been | :19:54. | :19:59. | |
understood for years. And you see a basic warming trend that correlates | :19:59. | :20:04. | |
with carbon dioxide emissions. Climate change science can forecast | :20:04. | :20:11. | |
what is going to happen to the earth in decades to come? If you | :20:11. | :20:18. | |
Trapmore Hague in the atmosphere -- if you trap more heat in the | :20:18. | :20:22. | |
atmosphere then you can predict something. But I cannot predict | :20:22. | :20:26. | |
what is going to happen in Ireland in the summer of Myatt they know | :20:27. | :20:33. | |
2058. The basic notion that the plan and is getting warmer and when | :20:33. | :20:35. | |
things get that the economic consequences is on solid ground. | :20:35. | :20:42. | |
There are those people, like the professor of statistics in | :20:42. | :20:47. | |
Pennsylvania, says that there is a lot we need to know about the | :20:47. | :20:53. | |
climate. These are her simplified representations of complex systems. | :20:53. | :20:57. | |
These are just approximations. course, I agree with that entirely | :20:57. | :21:04. | |
but the question is what evidence heavily absorbed so far? Could you | :21:05. | :21:09. | |
discount that there is a big freeze that might happen a new Ice Age? | :21:09. | :21:19. | |
:21:19. | :21:19. | ||
think it is unlikely. But possible? Do you want to face a bet? I do not | :21:20. | :21:24. | |
know if we're going to be around. I want to ask about the nature of | :21:24. | :21:28. | |
predictions. The odds of a big freeze are hundreds to one against. | :21:28. | :21:33. | |
When it comes to you, or something like climate change science based | :21:33. | :21:38. | |
on predictions and forecast, and you accept what the professor has | :21:38. | :21:43. | |
said, does one go to policy makers and politicians and public and say, | :21:43. | :21:46. | |
we will base their policies on these predictions and commit | :21:46. | :21:52. | |
billions and billions of dollars... A lot of policy positions have been | :21:52. | :21:56. | |
based on predictions that were on a much less solid ground and then | :21:56. | :22:04. | |
that, for example the invasion of Iraq. If, the decision to enter the | :22:04. | :22:10. | |
and have a common currency. And there much more systematic | :22:10. | :22:13. | |
uncertainties than in climate change. We have to make decisions | :22:13. | :22:21. | |
all the time. With the information available. A statistician from the | :22:21. | :22:25. | |
Copenhagen looked at climate change and accepts that it is happening | :22:25. | :22:28. | |
undeniably but says the narrow focus of reducing carbon emissions | :22:28. | :22:33. | |
that some governments have could leave future generations with major | :22:33. | :22:38. | |
costs without major cards to temperatures. Does he have a point | :22:38. | :22:42. | |
of as my this is not about the signs are predictions. This is | :22:42. | :22:47. | |
about what we do as or alleviation technique. If we have that debate | :22:47. | :22:53. | |
over here in Europe, in the US you have people still engaged in a | :22:53. | :22:59. | |
scientific debate. If you read the chapter in my book, I'm not someone | :22:59. | :23:04. | |
who says everything is always right on the climate science and be back. | :23:04. | :23:11. | |
It is still at a premature debate in the US. You say in your book, | :23:11. | :23:15. | |
you hope we might get more insight into planning our futures and | :23:15. | :23:20. | |
become less likely to repeat our mistakes. That is right.Therefore, | :23:20. | :23:26. | |
there is a purpose to predictions and forecasting. That is the whole | :23:26. | :23:31. | |
point stop on his swing, in my view, forecasting is not the most | :23:31. | :23:35. | |
critical thing in the world. But some of those same lessons you can | :23:35. | :23:38. | |
learn from those fields are instructive when it comes to | :23:39. | :23:42. | |
economic planning or obviously winning comes to anticipating where | :23:42. | :23:45. | |
the climate might go and what we can do to change that and mitigate | :23:45. | :23:54. | |
the effects. Wycombe and a lot from games, sports and politics. -- we | :23:54. | :24:00. |