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hello and welcome to instant genius a bite-sized masterclass in podcast form every Monday and Friday you'll hear World leading scientists and experts talking about the most fascinating ideas in science and technology today I'm Jason gooder commissioning editor of BBC Science Focus so today we have the privilege to be joined by Professor sir David spiegelhoff a statistician based at the University of Cambridge and the goto numbers guy let's be honest so David thank you so much for joining us oh great pleasure go sort of nerve-wracking but a pleasure so today we obviously going to be speaking about numbers and statistics so a topic a lot of people will think is maybe a touch dry but I
think in the next 30 minutes or so you'll change their minds well possibly I mean I'm a nerd and always have been but and I really love numbers and trying to explain what they mean and what they don't mean and um actually think it is interesting and should be interesting because you know if we look at just being a citizen today we're bombarded by claims on social media and from friends and contacts and everything about which are often based on numbers and so I everyone needs uh a capacity for dealing with them not to shy away and say oh I can't manage I was no you should be as just as embarrassed about saying that as saying you can't read so let's look at one of your one of the hats that you wear which is this
notion of risk so you read all the time in the news doing such and such raises the risk of this if I'm eating bacon or something you know so what they're getting things wrong you know what are they getting wrong whyless is they getting wrong it's the communication is there for purpose and so whenever I read a news story about or increases the risk of some something bad Alzheimer's or cancer or something like that uh I'm immediately skeptical because and not even before I look at the evidence I just think ah you know what are they trying to make me feel they just you know there are so many clickbait stories and uh they're trying to get me anxious a bit to read the story and say whoa do
you know what you know if you have yogurt it'll give you asthma or something like that so um you know these are great stories they're obviously very popular because the media covers them all the time and uh and but if you start looking beneath them you know there's two things you should look at first of all actually is this a big number because if someone says increases the risk that's almost meaningless because if the risk is incredibly low to start with increasing it's still it's still very low um so you want to know well actually does it increase it by an important amount and to an important risk is one of the first things to ask and the second thing of course is do you believe the number anyway so let's
have a look at that then what is an important risk you know is there a level that we can determine yeah well the point the standard thing is that when someone does an epidemiological study of what you eat and then what happens to you what comes out of that study is a relative risk it's how much that you know an exposure to the bacon or the alcohol or something like that raises the risk proportionately to what it was before so we hear that yeah eating a bacon sandwich every day or something will increase your risk of bowel cancer by 20% at oneth increase now that's a relative increase and so and that can sound quite frightening why God you know and I actually I do believe those
numbers I actually do believe those so I think it's based on some reasonably consistent science now but the crucial thing well 20% of what and so in order to interpret it and to know whether we actually should be anxious about this or change our habits we have to know how important it is and so we have to know well you know is this something that we should care about and so we need to know the Baseline risk the ab absolute risk rather than just the relative risk and so in fact you know out of 100 people right about six will get sadly get bowel cancer during their lifetime anyway if they even if they don't eat bacon and so that's what we're talking about is a 20% increase over six percentage points now
that's using percentage in two different ways one is a relative increase and one is an absolute number of percentage points I don't know any journalists who can do this they tend to get very confused by this one can sorry this one can this one can exactly yes yeah you're you're unusual I think I'm sure you can people find this really challenging to use percentages in two different ways and but it's one of the basic ideas of proportional reasoning that people try to teach in schools about using numbers journalists tend not to be very good at it but I think have learned increasingly that they should be asking about the absolute risks and then communicating it in the St way that's been really
explored deeply and shown to be effective as a communication device is to just to say what does it mean for 100 people and so you could say for 100 people who don't eat bacon six will get bowel cancer during their lifetime unfortunately and 100 people who do eat a bacon sandwich every day we're talking about a 20% increase over that six percentage points which takes it to just about seven percentage points M so and you can illustrate this beautifully by showing 100 little dots or little people and color them in and six become seven when all those hundred eat a bacon sandwich every single day of their lives so they're Al stuffing this thing in their gulb this great big greasy you
know sandwich in their G and one more out of those whole hundred will get you know will get bow cancer during their whole lifetime at which point you might think well I don't know past the Brin source so it's not actually that huge from an individual perspective now from a national perspective of the whole country that is important you know processed meat is you know does produce a measurable number of bowel cancers in the country and so from a public health perspective you would like fewer people to eat a lot of process me definitely however from an individual perspective it might be quite reasonable to reject that advice and say well I really like it I'm willing to trade off the enjoyment of eating this stuff
for this small increased risk of getting bowel cancer and so and I think you know well I think personally I think well when I because I do believe these numbers I've cut down on my process meat but I still love a bacon sandwich occasionally me too so how about these neat numbers like so recently there was a study saying that smoking a single cigarette takes 20 minutes off your life so how have they come to that number yeah I've done I used to do a lot of calculations about this I got to 15 minutes when I did it years ago so this isn't a new way to communicate stuff you could say you know I always said the first tra puts half an hour on your life and then the second one takes it off
again and the third takes off half an hour so it goes medicine Poison and so lots of other things so you know the first um know various other things but put you know your first 20 minutes of exercise a day um it basically puts an hour on your day um but and then after that after the first 20 minutes you know every half an hour of exercise puts about half an hour of your life so you better enjoy it because you know that's your that's the gain you're getting by doing it now as you said it's a bit nonsense because we got no idea an impact of a single cigarette or a single half hour now what we're talking about is averages over of habit habits that go on for years averaging over large numbers of people what is the linked
change in life expectancy and then we're reframing it in terms of the effect of an individual exposure now I quite like it but I've stopped doing it because I think that people might take it literally um instead of really just uh another way of framing an overall average risk on at a population level so let's have a look at averages then I think this is really interesting because there are several different ways of expressing an average so what are they and you know when are they best applied oh averages are quite tricky because I don't like averages people you know prot think statisticians oh are obsessed with averages no they're not they're obsessed with variability so just talking about
an average actually you know can be very misleading and um you know so if you think of you know how long I know let's take a simple thing you know I don't know Darren Brown on television flipping a coin 10 times and it came up heads every time then it turned out that he had filmed been filmed for nine hours flipping before he finally did the 10 flips in heads in a row and you can work out sort of how long do you expect to flip the flip these coins until you get 10 heads in a row and you get on average that's going to be about a th000 attempts because there's a one in a thousand chance of getting 10 heads in any particular attempt that's one average which is this mean average if you did it many times but then you got
another sort of you know which is the mode of the most likely time is the very first time you flip it that's the most likely time to First flip 10 heads it's the very first time because if you think of it um you know to get it first on the second attempt you're going to have to have not got it on the first attempt and so the probability of getting it the first time on the second attempt is lower than the probability of getting it at the first attempt so you got that's mode and then you got median which is you know 50% of the time um you know if out of a lot of people you know who's halfway along the spread of the distribution and um so these you know the median and the mean often if you got
a nice sort of symmetric C curve of a distribution they're fine they're about the same but if you got things like income they're massively different because the mean income if Bill Gates walks into the room is suddenly shut up the median income stays pretty well exactly the same so it depends on the shape of the distribution and a lot of distributions are not nice and symmetric and so you have to be really careful about whether you're talking about the mean the median or the mode so continue on from that you talked about Bill Gates there like one of the richest men in the world so let's have a look at outliers and so something that I spoke to you earlier about was what I call Uncle
Norman syndrome where people say well my Uncle Norman ate bacon and eggs he lived on bacon and eggs he smoked 20 cigarettes a day lived till it was 110 yeah there's always one there's always and there is always one because someone's got to be in the tail of the distribution that's the point that's why averages can be so misleading and why statisticians are interested in variability what is the spread and some people may live absolutely blameless and enormously healthy lives and die young and other people will you know as you said will be Uncle Norman and live for a long time good old Uncle Norman is what I say how lucky he was and I like the idea of luck Richard do one of the greatest epidemiologists who
co-discovered the link between lung cancer and smoking said whether you get cancer which is matter of luck it's a matter of luck some people will and some people don't however the chances are changed by how you live so Uncle Norm was extremely lucky to do if he did that because all the odds were against him um and so you would expect someone like him to have died but you know smoking 20 a day takes about eight years off your life if he's a slob and eats his bacon sandwich that's going to take a few more years off his life you'd expect him to die younger and uh but it doesn't mean everybody you know people there's huge spread and variability and that's great and that's fine but you
can't use that as a basis is for policy or even you know people might use it of course oh well I'm not going to take any noce of this health advice Uncle Norman lived till he was 110 yeah fine but you're not you're very unlikely to be as lucky as Uncle Norman was so let's have a look at luck then because I know you've written about this um it's really interesting there's actually several different types of luck yeah so what's that I love it well first of all I don't think of luck as being some objective Force out there that makes things go well or badly for us I don't believe in that at all I believe it's just um a label we give afterwards to things that um happened to us which are outside our
control uh were rather unpredictable and that had an impact either good or bad and so it's really useful just you know as he operate his way the chance hits us is another way to view it reading about this and writing about it has changed my life it's changed my perspective on life this idea of what they called constitutive luck who you born we appear in the world we got no control of who we are who our parents are what period of History we're born what social class into our genes you know where we are nothing we just appear we didn't ask to be born here we are by this extraordinary odd sequence of circumstances we're born at all I mean I could talk about my conception but maybe this is um not an appropriate Forum to
do this but you know the point is the fact that we're here at all is just a result of a whole lot of sort micro contingencies I call all these things that could so well not have happened but here we are and who we are born as has an enormous influence on the rest of our life you know we have you know our privilege or lack of privilege is huge I mean I was a boomer a born in a family had no money but we had enormous access to post-war welfare you know provision and uh I went to a really good I went to a good free grammar school I got this free education went to University that was free as well then I was at a period when I could pretty well choose what job to have for life with a final salary pension and meanwhile
watching all the house prices go up I mean it's staggering over which I had no control whatsoever and yet it's been hugely influential in my life so I think people underrate just how important it is who you're born as they like to think that their successes are due to you know all their personal characteristics and their qualities hard work well yeah a little bit but that's not the only type of luck the other type of luck is one is called circumstantial luck which is being at the right place at the right time or the wrong place at the wrong time you know like being on a plane that gets into trouble or you know just having to be in a car when someone else hits it you know just through no
fault of your own you just happen to be in the wrong place at the wrong time the example I give in my book is my grandfather who ended up as Brigade gas officer in 1918 just north of passionale one of the most dangerous jobs in the most dangerous places you could ever be you know that's where he ended up so and he lasted three weeks in the job which I thought was pretty good going before he got blown up but he wasn't killed otherwise of course I wouldn't be here by definition what he had was very good outcome luck which is the final bit of luck which is just how at that moment just how things panned out for you which could be very unlucky or it could be very lucky you know while you are in those circumstances so the
cir the constituted circumstan and out I find very helpful when you're actually trying to take apart good or bad when people talk about good or bad fortune that they've had so let's look again at sort of data reporting so I know the on talk about certain values trustworthiness quality yeah and value so can you break those down for us oh I know you can really get me going here and I should say conflict of interest I'm on the um a non-executive of the board of the UK statistics Authority in particular on regulation committee which oversees the work of the office for statistics regulation that produces the code of practice for statistics for this country I always used to say go this is one of the dullest documents ever you know if you
want for no it's not actually it's being revised and it's turning into really quite a good read and it is it's the thing that all official statistics in this country and an increasing number of non-official stat statistics are signing up to the code right should adhere to and it's got three pillars and the first one is trustworthiness then quality and value really important but it's trustworthiness that I felt I feel is the most important one and when I look for all the time in everyone who's communicating are they being trustworthy because too often those in Authority particularly scientists say oh how can we get people to trust us is ridiculous people aren't trusting us we're
scientists we know best how can we get people to trust us and I follow the philosopher or nor O'Neal who's been so influential on the statistics system who says what is the wrong that's the wrong question to ask you should be say asking yourself how can we be more trustworthy and demonstrate it other how can we deserve that trust how can we open ourselves up to be trusted not to try to force people to trust us and that's something they can they should only offer that up to us if we are trustworthy so this is she's a Canan philosopher this Duty ethics even before any idea of the you know utilitarian point of view of how can you actually get people to trust us you should be having the duty of being trustworthy it
and it's so simple so I've you know worked on quite a lot about this with colleagues and trying to Define what we mean by being trustworthy office of stats regulations has also got this very powerful idea of intelligent transparency of being open of being honest of um being clear about what the uncertainties are in your evidence um how good the evidence is of um of you when you're communicating not trying to manipulate someone's emotions but giving a balanced view you know you're trying to inform people rather than trying to persuade them of something and so much communication is persuasion and then actually you know taking a balanced View and you know pointing out um you know the if you've got something the winners and losers the benefits and harms not taking a
one-sided view all these seem basic ideas you certainly don't see them within when numbers get politicized and weaponized in society as so often they are so even before looking at the quality of the number when should be asking about well is this being communicated in a trustworthy Way the final thing that I'm fascinated by which um is and which is becoming increasingly noticed as an important thing is what you might call preempting misunderstandings or pre-b punking misinformation to know in kind of try to know in advance how people might misinterpret what you're saying and use it wrongly and then hit it hit that hard you try to preempt the misunderstandings by knowing your audiences by testing the materials because it's can be really
surprising sometimes how people can um misunderstand these things sometimes because it's deliberate it's disinformation rather than just misinformation but sometimes it's genuine misunderstanding so you need to preempt this you have to be able to say this does not mean X it would be wrong to interpret this in this way so you can't just leave that to the audience if there's a clear wrong answer you can't tell them exactly what to think but you can say this is not what it means and um and I I think that's very important and challenging because it means you have to you know your audience you have to listen to them you can't satisfy everybody there'll always be people who will won't believe you anyway but our research has shown that you know and we
we did experiments with you know randomized trials on thousands of people looking at things like vaccines and um uh nuclear power and everything like that showing that if you do communicate in this trustworthy way um preempting the misunderstandings balanced trying to inform rather than persuade um overall it doesn't make much difference in terms of how people trust the source but there's an interaction the people it does make a difference to are the people who are skeptical at the start about vaccines or nuclear power they recognize when they get a one-sided message and that's why they're so skeptical they oh we're just being told vaccines are safe and effective but if you give them a message that doesn't say
vaccines are safe and effective they actually says well vaccines you know can have problems and they're but they're safe enough and effective enough to use in some people in some circumstances people aren't do they recognize that you're actually being open with them and honest with them and their trust goes up yeah so you can increase the trust and skeptical people by being open and honest and trustworthy now the flip side of that I mean part you know as a bit of scientific research I think this is really powerful that the sort of thing that good experimental psychologists can do with randomized trials but the flip side of that it means that when government communicators or any communicators are giving a
one-sided persuasive message they are actively decreasing the trust in the group they are trying to reach the Skeptics they're making it worse there'll always be some people they can never reach but for the people who actually are listening then they're making it worse by doing that which I think is a really powerful message that comes from a scientific investigation of communication so how about trustworthiness in science and the way that the scientific method progresses and the way that people understand it so say we're told one day eggs are bad for you next day eggs good for you eggs are bad for you eggs are good for you and they say well I just can't believe anything these people say I better not
say what I think about most of nutritional epidemiology for frankly you know because that's people that's what people see in the news all it's so clickbaity as we've talked about and so newsworthy and they say they see oh constantly this is bad for you this is good for you coffee is bad for you coffee will give you cancer coffee's good for you and all this nonsense and frankly you know it's based on observational data following and it's it's not very rigorous science to be honest the quality of the evidence is low the confidence and the conclusion should be fairly low and so it's a shame that's is what so what science people do some of the worst science you would say is what most people see and what they don't see is the you know
proper scientific method which is very much not completely but is very experimental it's to do with setting up having ideas testing hypotheses of small incremental gains of um of dis agreement between scientists then trying to resolve it just watch that self-correction going on there are some claims because every so often people will do and then people check it and it doesn't work yeah we can see that has happened in Psychology in so many areas so that sort of self-correcting mechanism people on the whole don't see very much because they're being it's being portrayed in the media as if science says and science says one and then suddenly it says another no it's not it's that somebody's done some study
and making some outrageous claim that's not what science says at all maybe people ask starting to learn certainly journalists are starting to learn the um Power of might call metaanalysis or evidence synthesis where you don't just take one study you put a lot of stuff together and they really are grasping that really is a level of evidence Which is higher than most but even then if you put a whole lot of you know not very good stuff together you get something that is not very good so kind of tangentially related to that is this notion of surveys so this is particularly amusing when you get say um moisturizer and it will say sort of n out of 10 women said it improved the complexion within x amount you know a
month or something then you'll see a little star at the bottom and it'll say sample size 47 yeah it really was nine out of 10 yeah no I know people aren't DED they can spot the nine out of 10 cats when I was young it was n out of 10 cats prefer whiskers or something when they give two bowls of this stuff and um I think people can spot you know when people things are being done spuriously um for that so I'm not so concerned about the sort of idiotic surveys and also people and not you know the other thing of course people do surveys in order to plant a story in the news that's what a lot of PR companies that will do they'll do some crummy survey to show how much you know people like nuts
or something like that it's obviously funded by the nut Council or so um and I you know it's it's annoying but honestly I don't take much notice of it no the one of the big problems is you know surveys are incredibly important um and um you know during covid we were there was an infection survey that in this country which is the Envy of the world that where you actually knew how many people were infected with Co nobody else knew because you were testing vast numbers of people at Great expense and recording the results people aren't answering surveys anymore and response rates are plummeted and that is really causing a lot of difficulties you know the for example all the Employments
statistics in this country uh you might think we're based on statistics on labor you know counts and you know actual administrative data no they're based on a survey right and people aren't answering the survey like they used to they're trying to make it simpler online and so on and so and a lot of GDP you know measure measures for growth and expenditure and things like it's surveys of businesses so a lot of the economic statistics in this country are based on surveys rather than you know complete enumeration and um and response rates are going down so that is and it's not just in this country it's everywhere so that's causing a lot of soul searching among official statisticians which I'm kind of involved
in of to what extent can surveys be replaced by administrative data yeah um and that for example you know the you migration statistics in this country were hugely polit politicized and Incredibly important and up to recently they were based on frankly not a very good survey where people just got stopped coming into airports and you know I don't know if you've been stopped coming into an airport or aort once or twice yeah and ask what you're here how long you're going to stay for and things it's not a great way to um to actually work out how many people Mig migrants uh you know the migration patterns of the country so that again is now being supplemented by administrative data from
a variety of different sources so that's a official statistics is in a period per of transition of um you know moving away from the classic survey towards using the data that are just available so this is what we now call Big Data that sort of thing yeah it is I mean it's all the day for example for working out expenditure patterns and things like that rather than surveying Shoppers to get the transaction data from Super major supermarkets and so on so how about this notion of false positives which I've heard you talk about say what is that and what traps can it lay quite a big jump of topics but that's all right no false is incredibly important and you know they're important during Co is that I mean classically in covid is when you
you get a test and The Test shows positive result but yet you didn't have covid because of some uh maybe something else you had or something You' done to make the test show positive so and that's a real problem because if there's if something's quite rare you know if not many people got Co then suddenly false posit if there's loads of Co around a few false positives doesn't make any difference at all but if it's very rare the false positives can start being a major part of the people who are reporting positive and eventually you get to situations where most of the results are false positives and this happens quite a lot breast screening for example you know most of positive mammograms are false positives
because it's quite very rare and the test isn't perfect and so that's why you always have to have a follow-up you know visit of course and that's saying for a lot of screening devices that most of the um actual Things That Go triggered go ping you know are false positives because if you're looking oh say if you're looking for a needle in a hay stack there's a lot of bits of straw that look like needles right how about Randomness so this people often just throw this word around everywhere yeah yeah what does actually mean well it's very difficult I mean it's like a real struggle you know what does random I mean Loosely it just means unpredictable but if you going to use it at all technically it usually means that it obeys some sort of probability
distribution we know for example um I know Lottery balls you know go bouncing around in a drum and they fall out you know is that random well if it's random then we'd expect all the numbers they wouldn't all come out equally all the time but eventually they'll settle down to be roughly equal chance there an chance of every number coming out Y and we can check that and I did a recent analysis just checking all those numbers and yeah it obeys the rules of Randomness um you know and similarly random number generators that computers use you know for example premium Bond draws there's continual tests that they are in fact random because most random number generators don't have any in a
sense intrinsic Randomness in them at all they're completely deterministic they're just multiplying up two big numbers together and chopping off the last few numbers and so you know so most if you use a random number generator in your computer it's not random at all it's just it's completely it's just an algorithm that produces a number that does have a uniform distribution but you could run the algorithm again you get exactly the same number there's no intrinsic you know in a way uncertainty about it right so and simly Lottery balls bouncing around in fact that's you know not random because it's all they all just they're just balls bouncing around their Bay utonium Mechanic you know you could know exactly what's
going to happen it's just so staggeringly unbelievably complex it's completely unpredictable according to this property distribution so Randomness in a sense I is a useful term but it really essentially means unpredictable it doesn't necessarily mean what I would call stochastic that there's a genuine you know irreducible uncertainty about the situation yeah so how about this idea of people misinterpreting data based on well I was going to say correlation and causation but that's just one that's one no people inter Miss it's difficult you know um always say you know people ask me you know I've been working this whole area for 50 years and uh people ask me why do
why does everyone find probability and statistics so unintuitive and difficult and I just say well after Decades of study I finally concluded is because it really is unintuitive and difficult really is our brains are not designed and evolved to handle you know probability and Randomness in an intuitive in sorry in a in what sense mathematically correct way and I think there's a couple of reasons for that people have pointed to the fact that we um you know tend to overestimate rare events we tend to see patterns where they don't really exist because that's makes a lot of sense if you're in the you know in the jungle and you're you know you and rustling in the bushes you don't wait and exactly try to work out
you know think through all the possibilities you run away quickly so that sort of precautionary approach to potential threats could I'm don't know really whether this is the case could have led to this characteristic that people call apophenia which is the car the um tendency to see patterns where they don't really exist oh yeah but I think it's also the feeling that it's unintuitive just how clumpy Randomness is and the classic way is you I get a you know I get a handful of rice and I throw it in the air and it drops on the carpet it's not going to spread itself evenly there's going to be clusters and lumps all over the place and if I say if I then drew a map around it and said well these are cancer cases in the UK
people start interpreting the Clusters why are there so many there's a big gap there no that's just the way it goes roundness is not even plane crashes tend to come in threes as we've seen recently you know they really do tend to Cluster they don't evenly sprad once you've got a plane crash doesn't say oh yeah well we'll have to wait another two years before another no they tend to Cluster and the classic example of that is um you know birthdays you know there are 365 birthdays and yet you know if you take 23 people like all the people on a football game 50% of all football games there's two people on the pitch with the same birthday birthdays tend to Cluster the one I like which I think is
sometimes might not be that intuitive is if you got 20 people and you ask them the last two digits of their phone number okay there's a hundred different possibilities and you only got 20 people but there's an 87% chance that at least two of them have got the same last number it's very likely that there will be a match among those 20 people it's something I do with audiences all the time it surprises people the time and people why is it you know that seems really odd it's much higher than your intuition is and that's because Randomness does not mean evenly spaced it's very clumpy yeah so sort of sticking with this how about this what I guess you'd call the framing of numbers so if I say you're if you do such and
such you're 10% likely to die people are just think well really I won't do that you're 90% likely to not die then well I'll give it a go yeah no it's a standard with framing is incredibly important studied a lot in Psychology I know that I can make any number look big or small depending on how I tell the story what context I put it in you know I can make any number that frightening or reassuring just you know just you know because I learned the tricks and the standard one you mentioned is that you know in America they report you know heart surgery in terms of mortality rate 2% mortality rate in this country we have 98% survival rate whoa sounds much better doesn't yeah so in order you know to be trustworthy then whenever you're
reporting a percentage you should always do the percentage with and the percentage without you do the whole you bought the whole 100 two survivors and 98 no two deaths and 98 survivors and you show them both that's why little picture of 100 people with two colored in it's a really good way of doing it um but um there's other forms of framing that's called a difference between a positive and a negative frame so the one example I give all the time is that there was something called the 99% Campaign which had some posters on London bus stops and it said 99% of young London ERS do not commit serious youth violence it's a lovely story so I got a took a picture of this you stand
there and think okay that's a good news story it's a positively framed message there's two tricks to frighten you the first is to turn it into a negative frame that means that 1% of young londoners do commit serious youth violence so that's positive to negative frame but the next trick is to go from a percentage to what it means for the whole population to the whole group so there's about you know 9 million people in London there's probably about a million between 15 and 25 and 1% of a million is 10,000 oh my god there 10,000 violent young Maniacs in this city this is really frightening so that's and I could I maybe it's getting a bit political but I could do exactly the
same for the brexit campaign you know 350 million pound a week to the EU on the side of the bus I could make that look like a small number so you know the it's um and I watch for these tricks all the time and to be trustworthy you shouldn't do it you shouldn't be trying to frighten people or reassure them and the framing and the way you tell the stories I mean numbers do not speak for themselves the context the storytelling is absolutely vital in the impression they give and anyone who communicates numbers has to be aware of that and that's why I think you know statisticians because I do feel they should be involved in the communication of what of the work they've done need to be aware of this and should be an
integral part of the education for statisticians yeah so we've covered an awful lot here so sort of by way of summary do you have a sort of cheat sheet of people who are interpreting data or that when they're just presented with it oh yeah well I got that in um in my book artst statistics so I got that and the first one actually I stole from Tim Harford who also says this and I thought that's right even before you even look at the number you ask how does it how does this make me feel you look at your own emotional response to the number and that gives you insight into why you are being told that number yeah you know is it to frighten you is to reassure me then of course you look at
the source and you look at the trustworthiness of that source and you ask why am I hearing this why am I who has chosen to tell me this yeah and that's the very first thing even before you look at the number you think why am I being told this and so it s sounds cynical but it's it's just I think we just need to be aware of how many people are trying to manipulate our emotions out there using numbers which sound cold and hard and scientific no they're soft cuddly things numbers so and then I'd start asking well you know so in other words I'd ask whether the source is trustworthy yeah and then I'd be looking at the number of bit and ask whether the actual number is can I actually believe the number is it trustworthy often does
it actually represent what I think it represents you know what does it actually mean is the and then I can start looking you know where did it come from did they do a proper experiment or is this just asking 10 people or you know where's your source of the number and then you know the next one is the claim trustworthy based on because the number might be right but then people make some exaggerated Claim about therefore we should ban alcohol you know we should do this that and the other some unsupported claim so then you ask about whether the claim is trustworthy and that hugely depends on the context is this really a big number you ask and so on so in the last two you know I'm not sure what order best to ask about
whether you believe the number or whether you believe the claim they both sort of can be integrated in together but basically you ask about the source the number and the claim and you have to go through those and The crucial question was is this trustworthy so Professor sir David Spiegel halter thanks very much for that fascinating conversation oh well I could gone for hours so thank you very much indeed thank you for watching this episode of instant genius brought to you from the team behind BBC Science Focus that was Professor sir David Spiegel halter if you liked what you just saw then please do like and subscribe to the channel to receive notifications of upcoming episodes you can of course also find the audio only version of instant
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