Welcome back to the Oxford Mathematics Public Lectures Home Edition. This will be your fifth event.
My name is Alan Guerrieri, and I'm in charge of external relations for the Mathematical Institute.
Special thanks to sponsors.
Market access markets are leading quantitative driven electronic market maker with offices in London, Singapore and New York.
Their ongoing support is crucial in providing you quality content.
The current pandemic crisis, as underlined, the importance of mathematics in numbers not a day goes by without news about the reproduction number.
The number of case, the number of deaths and sadly, the number of death.
All these numbers collected, evaluated and presented as led to much confusion, manipulation and endless argument.
The problem goes straight to the heart of what statistic is, and it is used and sometimes abused in society more than ever.
There is a need for clear explanation of numbers and how they are used in decision making processes.
I'm particularly happy that Tim Harford has accepted the invitation to give a lecture about his issues tonight.
Many of you know Tim to his work and the Financial Times the BBC, but to his books,
is sure more or less on Radio four has been particularly influential in bringing light into the recent darkness tonight.
Tim will talk about his recent book, How to Make the World Add Up.
Thank you very much, Tim, for doing this. Please start now.
Hi, Tim Harford here, and thanks so much to the Oxford Mathematics Department for hosting this.
Talk about my book before I actually start talking about the book and answer the questions that you've sent in on Twitter.
One moment. And if you're watching this after the 14th of October, you can skip ahead a minute.
I wanted to talk about Blackwell's books The Amazing Bookshop on Broad Street, the heart of Oxford, when my book came out.
It sold out absolutely everywhere except Blackwell's books,
who had actually gone to the trouble of having enough confidence in me to order enough books.
They didn't instantly sell out, so I wanted to say thank you to them.
So I've teamed up with them to supply personalised dedications of the book, which isn't very easy in this time of coronavirus.
So if you're actually if you're anywhere in the UK, you can get black girls to send you the book.
If you're local, you can order the book. The book looks like this how to make the world that up, and it's up kind of them to to agree to do this.
But if you email Oxford at Blackwell's Echo Dot UK before the 14th of October,
if you include your name, your contact details, the person you would like me to sign the book for.
And if you've got a short message like Happy Birthday or Merry Christmas is not too early for Christmas,
presents just include that I will come into Blackwell's.
I will sign a whole bunch of books, I will personalise them for you. And then after I've done that, you can get in contact with Blackwell.
As you can go in, you can pick up your book, you can ask them to send it to you.
So please do that before the 14th of October 2020.
And then the book should be available after the 16th of October 2020 to collect or to arrange to deliver,
OK, personalised Blackwell's book message over.
Let's talk about storks and babies where babies come from.
You may have heard the the old saying that the stork delivers your baby brother or your baby sister.
That's where babies come from. And you may alternatively have heard a number of rather wild,
implausible and somewhat disgusting theories about alternative explanations for where babies come from.
You can put those out of your mind because the truth is babies do come from storks.
Storks do deliver babies. And if you doubt that for a moment, be reassured that I can prove that with statistics.
It's very simple. What you need to do is gather together a list of the number of babies born in each country and say,
Europe per year and the best estimate of the breeding population of storks in each country.
And you can just plop them a simple scatterplot babies and storks, and you will see just eyeballing the graph that there appears to be a relationship.
And when you do the maths properly, you will find a very strong correlation, far too strong to have been produced by random chance.
And in case you think, well, why would I trust that guy, Tim Harford, what's he ever done for the world of statistics?
I can assure you that this has survived peer review.
There is, in fact, a published paper entitled Storks Deliver Babies P equals zero point zero zero eight.
And without getting into all technical details, all the zeros after the decimal point mean this relationship is real?
It's not a coincidence. There is a strong correlation between storks and babies.
Now, by this point, you may be wondering how exactly the trick is done, or perhaps you figured out the trick, it's pretty simple.
Think about a place like Monaco or Luxembourg. There's not a lot of room for babies, and there's not a lot of room for storks.
Alternatively, if you mention a country such as Germany or Poland,
loads of space for storks and plenty of space for a high population, which means plenty of space for babies too.
So what this relationship is measuring is perfectly real. But what this relationship is measuring is basically big countries versus small countries.
It's a land area that's really being tracked now.
Congratulations. If you figured out exactly how the trick was done, but if you if you want to understand this sort of statistical conjuring,
there's no better book to read than how to lie with statistics,
which is an elegant little work was published first in 1954 and written by a journalist named Darrel Huff,
and immediately got rave reviews in the New York Times and elsewhere and went on to be so,
they say, the bestselling book about statistics ever published.
And I can see why the book has been so successful. I read it when I was a teenager, I learnt a lot.
There's great. It's funny, it's got cartoons. The examples are really good.
They're really incisive. They're written with with real wit. They have a dodgy grass, have a dodgy maps,
and the whole thing will really help you avoid being fooled by phoney statistics, by people who are trying to deceive you.
And of course, there are plenty of people out there who try to deceive you.
Daryl Huff lays out his stall very clearly. He says the the crooks already know these tricks.
It's the honest folk who have to learn them in self-defence.
So there's nothing wrong with that.
There's nothing wrong with a witty little book about how to lie with statistics, except that over the last 10 years or so,
I've become increasingly uneasy about that book and about what it represents and what
does it say about statistics and what does it say about our attitude to statistics?
The most successful book ever written about them is from cover to cover a warning about misinformation.
Once you start trading down that path, it becomes very easy to imagine that lying with statistics is all anybody ever does.
And I think that's a problem, because let me contrast how to lie with statistics published in 1954 with something else that happened in 1954,
which was that two British epidemiologists, Richard Dole and Austin Bradford Hill,
published some of the first compelling evidence that smoking cigarettes dramatically increases your risk of lung cancer.
Now, this is a really important result, and there were other scientists working on this result around the same time.
But it matters to discover that this very popular product that people use all over the world to discover that that's killing people in large numbers,
that it's extremely dangerous. Well, that's a little piece of information.
And that piece of information has contributed, of course, to substantial declines in rates of smoking in developed countries and a longer life
expectancy and reduced heart disease and reduced lung cancer and all kinds of good things.
And this is a life saving discovery.
This is tens of millions of people having their lives saved by war, by war, by a discovery that was made using statistics.
Because when you think about it, you realise you couldn't have figured this out in any other way.
That was one doctor in Argentina who treated a vast number of patients with throat cancer and lung cancer,
and he noticed that cancer rates were on the increase.
But actually, even just to notice that it would be an unusual thing,
much easier to notice that lung cancer is dramatically increasing through statistics and by statistics.
I don't mean anything terribly sophisticated to just mean somebody's got to count cases of lung cancer and write them down somewhere.
So even to notice it, even to see that there's a problem requires a statistical perspective,
even even quite a simple one, and to diagnose what is causing this huge increase in lung cancer.
Well, then you really need to ask the questions in a statistical way to ask them very carefully.
And bear in mind that Richard Doll didn't think that cigarettes were to blame.
Bradford Hill and Dole, by the way, both smokers, at least at the beginning of that research, think you know, the research cured them of that habit.
But Richard Dole thought that it was probably something to do with with the tarmac on the roads.
And another theory, quite quite a plausible theory,
is that it was pollution from cars because you think about this huge increase in lung cancer that's taking place through the late 1930s,
through the 1940s, into the 1950s. Well, what else is going on a lot more cars?
So it makes sense. But to really examine and to figure out, is it the cigarettes or is it the cars that requires the statistical lens?
So what Dole and Bradford Hill were doing was not incredibly complicated,
it did require care and it required effort at what stage they sent what they called a question or two about 60000 doctors.
They thought, Oh, if we if we contact all the doctors, that's that's a great group of people to contact for start 80 percent of them smoke.
But also, we don't want to lose track of doctors because they're all on the medical register.
And when a doctor dies, we'll know what he or she died of.
So they're a great group to examine. Well, this, of course,
made them extremely unpopular with doctors because nobody wants to be told that this thing that you're smoking is is killing you.
And at one Dr. Buttonholed Austin, Bradford Hill, I a dinner party and and said, Oh.
So you're the chap who wants us all to quit smoking, are you?
And Bradford Hill responded, No, not at all. I just want to see how you die.
If you quit smoking, I want to see how you die. And if you don't quit smoking, I want to see how you die.
So do what you wish. Quit or keep going. I will chalk up your death anyway, and it will be very useful to me.
I should mention Austin Bradford Hill, originally trained as an economist.
It's where he learnt his charm. So this is important work.
This is vital work. But now let's think about these two visions of statistics that have emerged in the same year 1954.
You've got Darel Health, who says it's a trick. I'm going to show you where the magician hides the rabbit before pulling it out of a hat.
And it's great fun and it's clever and we can appreciate it, but don't ever take it seriously.
Don't ever think of it as more than a game. In the very same year, Richard Doll, an Austin, Bradford Hill, using it not as a trick, but as a tool,
a tool to understand the world, and they realised that this is not a game or if it is a game, the stakes are incredibly high.
And my aim in writing how to make the world add up is above everything else.
To urge you not to be seduced by this vision of Daryl Huff, where statistics are a trick.
And instead to embrace the perspective of Richard Doll and Austin Bradford Hill to use statistics as a tool like
an astronomer uses a telescope or radiologist uses a uses an X-ray machine or air traffic control use radar.
There are certain things about the world. That we can't perceive in any other way.
And we need statistics to show us what's going on.
And I think the virus, the pandemic has underlined that point.
I mean, it's a it's a heck of a way to be proved right about what you were trying to say.
But to understand where the virus is and who's got it and how quickly it's spreading.
And is it? Is it spreading or is it in retreat?
And how dangerous is it? And who is it dangerous to? And how can we fight it?
What are the treatments that are most effective? These are all one way or another statistical questions.
And I think we learnt back in February March, when we were scrambling to make incredibly high stakes decisions with basically very little information.
We learnt that having the data matters,
having the statistics matters and when they're not available or whether they're patchy and incomplete, we suffer as a result.
So it makes me angry to just hear people going, oh yeah, lies damned lies and statistics,
so you can prove anything with statistics because this is a life saving tool, a life saving tool.
And we should not take it for granted. So one of the things that I'm trying to do in the book is to give people a little bit more confidence to
use that tool for themselves to to teach you certain habits of mind that I've picked up in 13 years,
presenting more or less questions to ask things.
To be curious about that will help you evaluate the claims that you see and think more clearly about them.
One friend of mine, Matt Parker, the stand up mathematician, said, Oh so teams, so you've written this book about statistics.
And I said, Well, no, I haven't. I haven't written a book about statistics. I've written a book about how to think about the world.
And for me, it's just statistics are an important tool for thinking about the world,
and I really believe that it's not as complicated as we often make it seem.
The questions we need to ask don't require a great deal of technical expertise.
Now. I spent some time talking about what happened in 1954,
and there is a reason why I'm so obsessed with this sort of period of history in
the in the early 1950s is because that was also the time at which the tobacco
companies started realising that the scientific evidence was coming in showing
that their product was dangerous and they had to figure out their own response.
And the way they responded, I think was quite brilliant in a in a dark way.
I mean, think about the problem you face. Your product is probably killing the people who consume it.
Think about how anxious people get about even even small things.
We worry about things like gluten these days, and this is a product that is killing people in large numbers,
like thousands of people every week are dying. What on earth is your response to that?
And what the tobacco industry realised was that people who smoke?
They would like to believe that the product is safe, they would like to believe that this thing that they enjoy,
that they find quite social, that they would find it difficult to quit. They'd like to believe that it is not killing them.
So they want reasons to believe, and you don't need to prove that cigarettes are safe.
All you need to do is give them reason to doubt that they're dangerous.
Scepticism, doubt is the weapon.
I think this was a brilliantly simple realisation when you look at the evidence, it does seem that doubt has a particular kind of power.
There's lots of evidence in the book on motivated reasoning and people reaching conclusions that they want to reach.
But one of the very first studies that I discuss by two psychologists called Carey Edwards and Edward Smith shows that negative arguments,
doubtful arguments. They flow with a particular fluidity.
That it's sure people are pretty good at coming up with reasons to believe what they want to believe,
but they're really good at coming up with reasons to disbelieve what they want to disbelieve, to doubt what they want to doubt.
And that's why slogans about lies, damn lies and statistics or fake news, they're so powerful.
You get to this point where we've all had enough of experts because the experts are telling us things we don't want to see.
We don't want to believe. Now we've seen these sorts of.
Doubt seeding strategies, we've seen this sort of approach used in all kinds of politics and debate over climate change and so on.
But it started with cigarettes.
It started with the cigarette companies sounding very reasonable, saying, Well, look, the world's a very uncertain place.
It's true. Experts disagree. It's true. More research is needed.
That's true. I mean, it sounds very reasonable. Sounds very scientific.
But in the end, you very quickly get from the Royal Society's motto Nullius in verba.
Take nobody's word for it to. Nobody knows anything.
And if nobody knows anything, you can believe whatever you want.
In the nineteen sixties, nineteen sixty five, how I remember rightly,
the US Congress held a hearing to decide whether cigarette packets should have health warnings on them.
Was a Senate hearing, and they called in all kinds of expert witnesses,
the epidemiologists and others to discuss the risks of smoking and the pros and cons of putting these health warnings on.
And one of the witnesses. Who was called sat down in front of the senators and started to explain that there's a correlation.
Between storks and babies.
And this correlation is pretty robust, but of course, that's not because storks actually deliver babies, it's because larger places.
Have plenty of room for babies and plenty of room for storks. The expert witness, his name was Darrel Huff, author of How to Lie with Statistics,
because his brand of scepticism searing into cynicism was absolutely perfect for the tobacco industry.
It's exactly the kind of message they wanted, so they hired him.
They paid him to work on a sequel called How to Line with Smoking Statistics.
And they persuaded him to testify in front of the Senate. And the senator leading the the committee hearing said, do you honestly mean to tell us,
you think there is this casual, a connexion between cigarettes and lung cancer, as there is between storks and babies?
And Daryl Huff replied, Well, the two seem to me to be about the same.
That's when I really start to worry about how to lie with statistics about what it represents because it's right to maintain a healthy scepticism.
It's right to notice that statistics are often used to deceive us.
But when we descend into that mode of thinking that it's all a trick, it's all a joke that you can't believe anything very quickly.
You end up siding with a guy who sits in front of the Senate commission and says
that cigarettes and lung cancer is basically just the same as storks and babies.
We, we end up in this sort of defensive crouch where we're basically not willing to believe anything on its merits.
We're not willing to weigh up the evidence. We're just scrabbling around for reasons to believe whatever we want to believe.
And I think that's a shame if I don't think it's a tragedy and I don't think it's that hard to push back and to stand up for ourselves and to say,
actually, this stuff isn't so difficult. I can tell a clear argument from an unclear argument.
I can tell the difference between someone who's seeking after truth and someone who's just trying to win some political debate or score a few points.
I can tell the difference between truth and lies. It's not that hard.
There is an old story about. Galileo and his telescope.
The time he was looking at the moons of Jupiter, the rings of Saturn, the Catholic Church was persecuting him and he said to the Cardinals.
No, I've got my telescope here. Just just take a look.
I'll show you I'll show you what I see about the Solar System and that the Cardinals wouldn't look.
They thought the telescope itself was full of trickery. Some kind of magicians to.
Now, that story has been exaggerated over the years,
but we still tell it and we tell it in this sort of smug way that we dismiss these outdated beliefs and this these religious extremists.
I don't think we should be so smug. Because I see people behaving like that around me every day.
Remember, I described statistics as as like a telescope, the way to see facts about the world.
Facts about the human brain. The human body. About the economy, about the environment.
About this incredibly complex world in which we live that way to see things we can't see in any other way.
So that's why I described them as a telescope. And every day I see people who won't pick up the statistical telescope, they won't use it.
They can look through it for exactly the same reason that allegedly the Cardinals wouldn't look to Galileo's.
Because they were afraid of being tricked. So don't be afraid, I urge you to have a bit more confidence in yourself.
I don't think it's as hard as we sometimes make it out.
And the 10 rules of thumb that I've outlined in the book, I hope, are rules that anybody can use.
So have some confidence in your ability to distinguish truth from fiction.
Have some confidence in your ability to think clearly about the world.
A lot of it is simply about wanting to know, wanting to understand.
And I think all of us should be willing to pick up this statistical telescope.
And to look around, you'll be amazed what we see. Thanks for listening.
I have received many, many questions on Twitter because I asked some of the questions were along the lines of Why are you taking questions on Twitter?
Why can't you give the talk live? Well, you know, there are different ways to give talks.
We're all trying to experiment and adapt in this, this COVID era.
I'm really delighted to have been asked to speak at the Maths Institute, and I hope you've enjoyed what I've said.
But I do have these questions and I wanted to leave quite a bit of time for questions because the questions that have come in, I think,
are a really great way to get into some of the ideas in the book and some of the practical tips that I outline that I outline in the book.
I should wave the book around. Shouldn't I go out to make the world that up?
10 Rules for thinking differently about numbers. So, OK, questions.
Question from Sarah Brighton. What are the best questions to ask when we see statistics used in the news to help understand them?
It's a great question, partly because basically the the 10 rules in the book, the 10,
they're not really Ten Commandments, the ten habits of mind, plus the golden rule at the end of the book.
I mean, they are my answer to the question. The book is basically the answer to this question.
What questions should we ask and when we see statistics used in the news?
Let me give you a few examples. Take a little bit of time to to to focus on this question because it is so central to the idea of the book.
So the first question I would ask, I think, might surprise some people.
So I begin the first chapter of the book by telling the story of Abraham produced Abraham.
Radius was a great art critic in the late 19th and early 20th century, and I describe him sitting in his in his villa in Monaco,
enjoying a well-deserved retirement and enjoying the admiration of the art world for his expertise.
When in the nineteen thirties, a charming Dutch lawyer named Gerard Boone visited him and showed him a painting.
And asked his opinion because Boone said this painting has recently been discovered in from a private collector,
and we think it may be by your hands for me.
Yeah, that's for me, the great Dutch interior painter and Mr. Brady, as you are the world's leading expert on Vermeer.
So could you tell us what what you think of the painting and Abraham Radius was spellbound, almost literally.
He he later wrote shortly afterwards wrote in an art magazine,
Burlington He that when he saw this painting, he said I had difficulty controlling my emotion.
He said he felt that he was in the presence, not just of of a Vermeer,
but the masterpiece of Johannes for me, of Delft, quite different from all his other paintings.
And yet, every inch of Amir. He also described it as uncorrupt just the Dutch word for virginal and pure and uncorrupted and untouched,
which was ironic because basically it was a fraud and it was not only a fraud,
it was a really nasty, vicious fraud by a really nasty, vicious man whom I describe in the book.
And it wasn't even a very good painting. And the thing the one thing you can say about Your Highness for me is that paintings are good.
I he's really good at what he does. The paintings are magical.
So how was it that Abraham Brady was this great critic, this great expert was fooled by this crude forgery that wouldn't fool you or me.
And the simple answer is in that line, he wrote for Burlington Magazine.
When I saw the painting, I had difficulty controlling my emotion by exploiting Abraham Brady as his wishful thinking,
by painting the picture that he knew Abraham Radius wanted to see.
The forger managed to bypass Brady as his expertise.
It didn't matter how much he knew about the technical details of painting.
He wanted to be fooled, and in fact, his expertise in this particular case only made things worse.
She gave him new reasons to believe in a painting that he should never have believed in you, or I would never have fallen for this.
But Abraham Brady, as the expert did. Now why am I telling you a story about art forgery?
Why am I beginning the first chapter of my book, which is supposed to be a book about numbers, right?
That has no numbers in it? That is purely about, well, about painting and about perception.
It's because of emotional reactions come first.
If we've learnt anything from the last few years, the Brexit referendum, the election of Donald Trump, we've learnt that.
We believe what we believe because of who we are and how we feel, our emotional reactions and our preconceptions.
So there's no point in me writing a book that will tell you how to solve technical problems in statistics
if it can also give you the emotional tools to get past your wishful thinking and see the world clearly.
I think the one thing that we've all learnt over the last few years is that what we believe is overwhelmingly determined by what we want to believe.
We're all influenced by ah, by our friends, by our cultural identity, by our political preconceptions.
And so, of course, our emotions matter. So the very first question I would ask in answer to Sarah is how is this statistical claim making me feel?
Is it making me feel angry or defensive or vindicated?
Can I do I see this as as some ammunition for some argument that I want to make?
Because if that's how you're feeling, you're probably not thinking clearly. Of course, we should be influenced by our emotions.
Our emotions are important. We're social beings. It's fine to have preconceptions.
It's fine to pay attention to what our friends think, but we have to be aware of that.
So I'm advocating statistical mindfulness. I realise I now sound like yoga with a calculator.
Statistical mindfulness. Notice how the claim makes you feel.
And if you're if you're if you are aware of your emotions, if you're aware of that emotional reaction, you take a few seconds to observe it.
Maybe let it subside.
I think you're going to think more clearly about how to then go about evaluating the claim and whether you should be sharing it or not.
And second question I would ask, it's a serious question. What are the best questions to ask when we see a number in the news?
The second question to ask is what does the name actually mean by which I mean something quite specific, like what is actually being counted?
So for example, we see these of these daily coronavirus cases reported every day in the UK to understand that.
You know this these are official cases, they're being processed, the tests are being done,
and the tests may be being processed with a delay of several days.
They might have a test gathered before the weekend, and the test is that being the test results published after the weekend?
That sort of is just straightforward knowledge about what this number represents versus, for example,
the Office for National Statistics Infection Survey, which is this is not an official case count.
This is an attempt to randomly sample the population and to estimate how many people out there have infections.
It's a different methodology, I would say.
Probably a better methodology for estimating how how much the virus is out there just to know what's being measured,
what it means during the financial crisis.
I got so, so many claims that mixed up the deficit and the debt you could argue.
People would argue about whether the claim was true, whether the number was right.
And so you can actually even understand what it is that you're talking about.
Or another example, it's in the book an article in The Guardian that talked about suicide and self-harm.
And actually suicides are different from self-harm,
and the definition that was being used for self-harm was was actually ambiguous that the article didn't discuss what was meant by self-harm.
And then I had to go and talk to the researchers and the researchers in the end said, Well, actually,
we don't know what's meant by self-harm either and self-harm as as defined by the the people who responded to our survey.
So it's whatever they think now, it's nothing wrong with that.
But just understanding what the definition is, what's being measured, what's being described before,
before you get into the mass, before you get into evaluating the number, that's really important.
And the third question that I would ask a very simple question is how does this fit into the bigger context?
So for example, if you if you see that there have been 6000 daily cases of coronavirus to ask questions like,
well, how does that compare to a week ago?
Is there a rolling average? How does that compare to other countries?
What about France? What about Spain? How does it compare to six months ago?
Can we make the comparison with six months ago? Are there other reasons to believe that the comparison doesn't work?
Now, of course, you know, as an individual.
Some of this stuff is a lot of hard work. You don't want to have to go through all this work every time you see a number,
but you should certainly see some evidence that somebody else is doing that work for you.
So if you're reading a social media post, listening to the radio, watching the TV, reading a newspaper article,
if you're hearing that sort of explanation, that sort of context, what was down, what was measured is the number going up or going down.
What is it per person or per million people?
If you're seeing that, I would be reassured that you're hearing from someone who's trying to help you understand the world.
And if you're not getting that context, you're you're hearing from someone who's trying to get you excited or riled
up or sort of win your vote or defend themselves against a political attack.
And that's not the same thing, and it's not helpful. Right?
Another question. Thanks, Sarah, for that question. Dave Bradshaw.
This leads into the previous question. Dave Bradshaw asks how much of a worry is it when he puts this in square in scare quotes?
How much of a worry is it?
When leading epidemiologists put out misleading information about the infection fatality rate of the virus never bother to admit the mistake,
and he links to a particular video.
But I'm not so interested in that particular video because there's lots of this about there are we focus on the virus fragments.
There are a lot of people around, some of whom are eminently qualified and some of whom.
It's not that amateurs or their politicians,
but a lot of people about who have become very concerned with winning an argument with making a particular case for a particular view of the world.
And whenever I see that I, I start to worry because it becomes harder to really evaluate what's going on,
becomes harder to see clearly becomes very tempting to score cheap points.
It becomes very tempting to play for people's emotions.
And as we already saw with my answer to Sarah, you know, getting people emotional does not help clear thinking.
And you start getting tempted to cherry pick data to strip things of context and to be unfair.
So some of the. Some of this sort of debating this point scoring is done by experts with some expertise, but it still makes me uncomfortable.
And some of it is just complete nonsense.
So for example, on the side of people who who say we need to be very, very worried indeed about about the virus, I see lots of stuff about long COVID.
In other words, people who have lasting and maybe serious symptoms who nevertheless didn't die, but very little about how common this is.
And we need better data, of course.
But if you're just tweeting examples of lupus, this person and this person suffered this long running debilitating disease.
We haven't really learnt anything because millions of people have had the virus.
Tens of thousands of people have died of the virus.
The fact that you can give me a single example of someone who's suffering long term health effects, it doesn't help me understand.
Now I want to understand long home and I want to know how prevalent it is.
I think it could be a very serious problem, and it's certainly something we should investigate.
But I want to explore that with the mindset of someone who's seeking after truth rather than someone who's trying to make a particular point.
And you see on the other side of the debate, the lockdown sceptics, I think they often call themselves again.
I'm not very loud and we've skipped from one point to another or the infection fatality rate might be super low because
maybe everyone's kind of already had it or we just on the brink of herd immunity or died with COVID vs. died of COVID.
And now all this nonsense about false positives and in each case, there's somewhere underneath those arguments.
There's a really important point to make. False positives are a thing.
Asymptomatic, undetected cases are a thing.
Herd immunity might be more prevalent than we think, but when it's used again to make a particular argument, we're getting stupider.
We're not getting smarter. So it disturbs me when I see anybody arguing for a particular case rather than just trying to explore the truth.
And you can see it's not hard to see. What you know, who's who's doing, what goes on on this sort of thing.
A question from Sally, Steve and Sally Stephens says should it be mandatory for MPs to have some statistics training?
Probably not because, you know, in the end, it's a democracy and we voters need to take responsibility for who we elect.
Right. But I think it would certainly be helpful for MPs to have some statistics training two separate points.
One is that we just need a more diverse group of MPs. And by diversity, I refer to all kinds of things.
You know the things that you're probably thinking of.
So and people from different ethnic backgrounds and from different classes, different parts of the country,
different educational backgrounds, men and women, different sexual orientation and peace with disabilities?
We need all of that. We also need peace with different educational backgrounds.
So a lot of lawyers in parliament and quite a lot of people who studied history and classics and even a few who studied people like me,
there's nothing wrong with any of that.
But it would be good also to see MPs with mathematical training, statistical backgrounds, chemistry, physics, biology, medicine.
We need all of these things. The world is a complicated place. The decisions that MVP's have to make on our behalf,
they're complex and you're only going to get the best decision making when you have that real mix of different expertise in the room.
So it's that second point to make is the research done by Phil Tetlock, Barbara Melas and Don Moore became famous as the super forecasting research.
There's a great book called Super Forecasting by BI Tetlock, and Dan Gardner is his co-author.
One of the things that they find is that even quite brief statistical training, like an hour of statistical training,
really helps forecasters to make better predictions about the world more robust predictions and really understand what's going on.
So there probably is a case for some statistical training.
I don't think it's that hard. I mean, another thing we could do is maybe we could.
Just everyone could mail a copy of how to make the world add up to their local MP.
Great idea. Now you suggest it. But this stuff doesn't always require a Ph.D. in statistics or advanced mathematics.
A lot of it is basic critical thinking skills, plus the motivation to understand what's happening rather than try to win an argument.
And of course, MPPs are highly motivated to try to win arguments, which is a problem and not a problem I can entirely blame them for.
That's just the way things are, right? Oh, question from Will Moy, who runs full fact.
What stat do you wish you knew and why?
She will know if Will is aware of this, but he's in the book,
quoted as saying that we know more about golf than we do about victims of serious crimes such as rape and sexual assault and assault and murder.
Why do we know more about golf than we know about the victims of these crimes?
And because the the survey that measures physical activity, sporting activity is much bigger than the crime survey,
so it's higher resolution enables you to make statements about local areas, enables you to make statements about rare activities.
But this isn't because some civil servant or some politician at some stage said, You know what?
We've got the statistics budget, and we really think sports are more important than crime.
It's just because the The Victims of Crime Survey has been running for a long time at a particular size.
And then when the London Olympics came along, there's this big push for sporting participation,
a big budget to to encourage sporting participation and therefore a big survey of sporting participation.
Nothing wrong with that, but just goes to show that we sometimes we make these argument.
We collect these statistics almost by accident. We're not very considered about what we measure and what we don't.
And I should shout out for an iPad. Will Smith's blog Missing numbers, I think, is missing numbers dot org, but you can find it,
which basically just tries to track things that maybe the government should be measuring and and isn't.
And in particular, things the government used to measure and then stopped measuring and often didn't even notify anybody to stop measuring it.
The data there was just disappeared. For power not to collect numbers is is very important.
All that said, OK, so to answer Will's question, what state do you wish you knew and why?
Well, when I was finishing off the book in late April, the answer was very clear it was the infection fatality rate,
and I wanted to know the infection fatality rate of coronavirus because it was still, I think, highly uncertain and hugely consequential.
Some people were emailing me and saying,
you said in your latest column that the infection fatality rate might be one percent, but it's clearly one in two thousand.
I mean it. If it's one in two thousand, everybody in the UK had coronavirus twice in March,
April and May, there is no way to make the deaths come out in the other way.
This is clearly not one in two thousand so that that was the number I really wanted.
I think we've done a lot of work figuring out what it is. I think we now know it was about one percent and it has been coming down.
It's been coming down because older people who are much more vulnerable and people who
are vulnerable for other reasons are doing a better job of hiding from the virus,
and we're doing a better job of shielding them from the virus. And also because treatment is is getting better.
Possibly also because we're doing a better job of shielding people from from high exposure to the virus so that
these various things are helping to bring the infection fatality rate down from one percent to half a percent,
which is obviously important. Still, a really nasty virus and incredibly dangerous for four people over the age of 70.
So that's the number I that's what I would have said when I was writing the book.
If you'd asked me what number now? So there's a lot in the book about algorithm.
There's a long chapter on algorithms, and one of the things I really wish we knew more about was the effectiveness of
algorithms that our politicians are often deploying to make decisions about us.
So we have algorithms that are deciding who gets bail and who doesn't get bail.
So we saw this summer algorithms that replacing A-level and GCSE exams.
And I think the algo shambles of the summer really indicated part of the problem that
somebody had managed to persuade politicians that this very painful decision that made,
which was to cancel everyone's exams didn't have to be painful at all because there
was going to be this magic algorithm that would just give everyone the right grade,
which when you think about it, it's clearly impossible.
How could an algorithm give you the right grade for an exam that you haven't sat and you're not going to say it is impossible?
And I think if politicians had recognised that back in March, they could very quickly have said,
OK, what are we going to do about the fact that there is no way to give people that grades fairly?
What are we going to do to survive a world where we don't have that information anymore could be postponed?
The exams can be start expanding higher education?
What do we do? But instead, people just thought, well, the algorithm will be fine.
So I want politicians to have a better sense of what these algorithms can and can't do.
But that then leads into this missing statistic that is so often missing that will boy asks about,
which is very often we don't have any proof of effectiveness of an algorithm.
We don't see algorithms evaluated with, for example, with rigorous randomised trial,
which you would often want to do if you've got a medical decision making algorithm or a
policing algorithm to compare what the algorithm is doing vs. what human judgement is doing,
and to say, yeah, the algorithm is or is not making the decision more quickly, more accurately,
etc. So you made these grand claims for these algorithms that are commercially secret that can't be evaluated by independent experts.
We want to see what's going on inside the algorithm. We want independent scrutiny and as much as anything else,
we want proof that it actually works and we don't take vaccines or drugs without proof that they work.
Why on earth should we let an algorithm make decisions without proof that it works?
I'm looking at my clock. I've talked for too long. I've talked for far too long.
I'm going to shut up now and thank you for everyone who. To everyone who sent in questions on Twitter.
I'm sorry, I didn't get to all of them. There were loads of good ones. The book answers some of them.
You can always drop me an email if there are others that you want answered, and I will do my best.
Thank you so much to the Oxford Maths Department, the Mathematical Institute, for inviting me to speak.
Thanks again to Blackwell's books for supporting how to make the world that up.
And final word to all of you. Be curious about the world.
Try to think clearly. Try to ask the questions that you want answered whenever you see one of these statistics.
Don't use statistics as weapons don't stand for other people using them as weapons.
Use them instead. As a kind of telescope to see the world more clearly, you can do it.
I believe in you. I think we can all do it, and we're going to make it easier for each other if we all band together on this point.
So thanks very much.