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The Untruthful Art: Five ways of misrepresenting Data - Alexander Arvidsson - NDC Oslo 2021

Mar 21, 2024
support their narrative. What can we conclude from this

data

? She doesn't have his own voice. The

data

is not going to tell you that you don't decide, it says so or the people who try to tell you so, so don't do it. I never trust data and, for example, this was posted on Twitter, I think it was three or four months ago. I'm just. I stumbled upon it and then I decided that yes, this will be included in my session, so Bernborg is Danish, I think science denier or client of a climate skeptic client or climate change denier, he put this up and said: Well, apparently this isn't that big. problem that people think is unfortunately for him, Andrew Destler, a professor of atmospheric sciences and climate scientist in Texas, found this and said, "Wait a second, that's a little strange because yes, in the 30s they were hot, but not so much". and why the hell is the 2010s not showing up at all something is strange now this is epa data so the environmental protection agency has released this this is correct, validated and verified data why it looks like it looks like a couple of reasons to To start with, this is plotting what is known as the heat wave index and if I were to ask any of you what the heat wave index would be, we would have a lot of great answers, none of them would be correct.
the untruthful art five ways of misrepresenting data   alexander arvidsson   ndc oslo 2021
I'm reading word for word a heat wave. The index counts the occurrence of four-day heat waves with temperatures exceeding one over a 10-year recurrence. Well, that's kind of a different view of things, so by choosing this extremely strange metric, you can show what would happen if we changed the way we look. heat wave to something more reasonable instead maybe let's look at the actual temperatures and compare that all of a sudden we're going to see this uh so the 30s yeah it was warm it wasn't that warm and then we had a gradual rise back a in the 2010s and 2020s, the funny thing is that there are actually two selections on this one thing, the first was to choose the very dark metric, the second, this is just the United States, the world looks a little different, it is funny, so no, Mr.
the untruthful art five ways of misrepresenting data   alexander arvidsson   ndc oslo 2021

More Interesting Facts About,

the untruthful art five ways of misrepresenting data alexander arvidsson ndc oslo 2021...

Lomborg, you can trace. Back under your stone, what can we learn from this? Well look at the agenda, someone is al

ways

trying to sell you something if you don't know what they are selling you could be the product. I think you've heard that before and consider the answerers, if I asked a very homogeneous group something and asked another group that has nothing to do with the first group the same question, we could have two completely different answers, just saying and finally I try to examine the base data, if possible, we have it. With a lot of data available to us, it can be a difficult proposition to find your way and find the base data, but most of the time it is there and you will find that many times what is reported is not necessarily supported. based on actual data, so let's look at comparisons, comparisons when comparing things, they are a classic way of driving a narrative, there is a saying that one would compare apples with apples, but I will show you some creative

ways

to compare both, basically apples with trombones and in case anyone was wondering, it's a six valve military trombone from 1866.
the untruthful art five ways of misrepresenting data   alexander arvidsson   ndc oslo 2021
Comparing things is hard, it's even harder if you lack basic literary skills, literacy skills, how many weeks do we have in August again? Do you think I found this image? What television station? Yeah, Fox News, anyway, back to the relative dangers of life. It turns out that living is actually terminal, you won't get away with it, but that's a different story. itself, so this is an image that shows the relative danger, that is, the most dangerous cities in the US. We have this little infographic that shows the relative danger. I don't know how danger is measured, is it in kilos or meters, I don't know. you know and this is what's known as fluff, there's data in here but there's also a ton of cool pictures and graphs and things that do nothing but make your eyes go like this, you don't know what you're looking at so yeah If we had to clean this up, remove all the fluff and just look at the actual data, this is what we're going to find, so Chicago is at the top here and the 10 most dangerous cities in the US.
the untruthful art five ways of misrepresenting data   alexander arvidsson   ndc oslo 2021
Are we missing something? Yeah, where's my? scale where is my x axis someone misplaced my x axis, so let's set it to actually be the number of homicides by city for 2019. Do we have a problem with this? I got it. I was never very good at geography, but even I know that. that there is a small difference in size between Chicago, 8.9 million people, and Baltimore, with 2.3 million people, so if you compared the absolute number of people killed, you would probably see some differences in the numbers. What do we do well? Yes, We're going to go back and look at the data, but we're going to control for the size of the city.
We can do this by simply stating the homicide rates per 100,000 residents per city. Where did Chicago go? Hey? Chicago is not that dangerous. apparently well baltimore is even harder and san louis you don't want to go there but again the fluff didn't show you this and if you don't control precisely this is what you end up with and then we have this this was sent by a former colleague of mine who I have said since the beginning of this pandemic that I am not going to touch the Kobe 19 data with a 10 foot pole because the data is horrible, you can't compare the Kovid 19 data, that's all.
I will say that he sent me this and said this is an interesting example of what, well, let's see what this actually shows. This is from the UK and these are the hospitalized patients and we can see that Scotland had a pretty bad day and what it is. It's happening in Northern Ireland, I don't know, look at the Y axis, yeah, so at the top of England we have 30,000, at the top of Northern Ireland we have 700. You can't compare these, what do you think? Is it going to be good for people? They are predominantly visual, so let's look at the shapes of the images and apparently it looks like the Northern Ireland part is a bit iffy.
You don't compare them like that. You need to have a common base. This is the BBC. Someone should know better, so we can look at the income and as we can see in my company, I'm rich, I'm getting richer, just look at my income, it's going up and down, it's amazing, uh, but if we had to. um, oh, I don't know, look at the axis and again, some kind of large sum of money, 500 million, a billion, aka a billion, one and a half billion, something's a little weird here, just go on, right? because? Well, this is because we're looking at accrual revenue and if they didn't tell you that it's accrual revenue, no, we look at it again and go, yeah, it seems reasonable, this is what it really looks like, but since it's accrual, it's necessary. we have a serious enough deficit for the line to go down, so be careful when comparing cumulative or absolute income or numbers, compare apples to apples, don't put any trombones in your data taken out of context, that's very strange. sentence compared to what always has a baseline I am the best speaker of all time compared to my cats my cats have never been on stage so yes you need to compare with something reasonable my cats are unreasonable and please consider absolute versus community cumulative increases you could look at the same thing on a graph but look at the scaffolding look at the text look at the description what we are what we're trying to show you and it's time to pivot to worse correlated causality so what does this mean?
Causality This is the action of causing something, I am sure you have heard that correlation does not imply causation more than once and that is extremely true, but it is also something that very few people realize what it means, I will show you to start, did you do it? I know that the per capita consumption of cheese in the US and the total revenue generated by golf courses each year actually correlate, but they do not, they are not causal, I can guarantee you that there is no causality between consumption of cheese and the revenue generated by golf courses there is nothing that drives the other, it turns out that they correlate, they do not drive each other, keep that in mind because I am going to show you something terrible: this is glyphosate or as it is known More commonly as roundup roundup is a herbicide released on the market by the monsanto corporation in 1974 and has been called many things, for example, in 1996, the attorney general of new york ordered monsanto to remove advertisements saying that roundup is safer than table salt.
It was also virtually non-toxic to mammals, birds and fish, the debate about whether this is carcinogenic or not is still going on today and I mean it's hard to determine what is true or not because there have been several lawsuits about it and excessive amounts of money have been paid to people for it. for claiming that this caused cancer, I don't know, but in 2015 the world health organization's international cancer research agency, iarc, classified glyphosate as probably carcinogenic in humans, in contrast, the European safety authority The food industry concluded in November of the same year 2015 that the substance is unlikely to represent a carcinogenic threat to humans, I don't know and that's what I don't know, no one knows, but the lack of firm evidence does not mean that people do not create these kinds of images, so um and while we're at it, why don't we throw genetically modified crops under the boss?
There are some strange things happening here, both things we see and things we don't see. I mean, there are some things I want. I want to point out how, for example, we can have minus 10 of something. How can we have less on a scale like this? Well, the reason it is less is to have the intersection, that is, the way the lines intersect and cross each other to look good, this is a classic manipulation of the visual, the goal is to show that there is a connection between the incidence of thyroid cancer and the amount of glyphosate applied to corn and soybeans, these are correlated, there is no causal link. that has been established, has anyone ever seen this gentleman?
This is a British doctor named Andrew Wakefield and in 1998 he published a study linking the mmr vaccine to autism. That study has been completely trashed, in fact he has been expelled from the British Medical Association. Unfortunately, he is no longer allowed to practice medicine, that didn't stop people from thinking that vaccines cause autism and yada yada and let's think about this, what kind of consequences does this information have? Well, the global and vaccine movement that is running rampant, especially under greed. conditions, but even before Kobe, when it comes to mmr stuff, they got a lot of wind in their sales and we can use that data to drive an agenda and combine probably very innocent things like this so we can see that the sales of organic products food is increasing we can see that the prevalence of autism is increasing that's all we can see we don't know why we don't know why organic food sales are increasing we don't know why the prevalence of autism is increasing that's all remember that people see what they want to see consider a family having a child being diagnosed with autism that is a terrible experience and it is not unreasonable to think that in their grief in their fear in their anger they go to the Internet and try to find out why their son has been hit with this, why has this calamity happened to them and they find that they find something to hold on to, we are seeing data, but it is not as much data as the human being is. factor in the human condition, everything has consequences, suddenly they have something to hold on to and something to attack and we just go from a fairly innocent image to people on the streets, this is extremely dangerous, what can we learn from this?
Well, just because you can't. It doesn't mean you should do it just because you can compare things and just because you can make fun correlations doesn't mean everything you do has consequences and the scale will decide the curve if I want. I can make almost any curve intercept another. curve as long as you play with the scales again don't do that have zero as a reference point there is a reason for that and eventually you forget everything about this session remember this correlation does not equal causation just because two things happen at the same time right?
It doesn't mean that they are happening because of each other, it's time to dive into the fifth and final part. I must say that we are going to start with the bratia genada indian party or bjt they put something funny on Twitter. They have a pretty clear agenda, right, they are politicians and their agenda is to make sure that Narendra Modi remains Prime Minister of India and like many other political organisations, they play fast and loose with numbers. I mean, apparently, the voters are so-so. as smart as a brick, so they put this on Twitter. I am the first to be very clear.
I'm terrible at math, but even I think something isn't quite right here. Kinda okay, so what do we do right? Delete it, let's remove all the weird stuff we're seeing again. It's because of the fluff that we don't immediately see that something is wrong, so let's remove the bars. There we have numbers without thebars. The mind has nothing. holding on so suddenly we can look at the progression of the numbers and say yes, that's reasonable and we can do it even more than this, what if we put the bars back to how they should have looked, huh, that's a different message.
It's not like that, this was obvious, this is what we should have seen, this is a wonderful example of deceiving or just lying to your face with a picture, they didn't even try to hide it, it's clear for everyone to see, consider how many people. actually it did in many ways it doesn't matter if you try if you're convincing everyone as long as you're convincing someone well you kind of made your point clear oh we're going to the US and this is a bit of a topic delicate I'm not dumb enough to dive into the latest election you are walking on a minefield this is pretty bad so in 2012 there was an election where um barack obama the current democrat president was challenged by the republican candidate Mitt Romney, okay, so this is a map of the US, well, it's the continental US and Hawaii and if we were to color the states that voted Democrat blue and the states that voted Republican in red, this is what we're going to see, there are a lot of Republicans here. a lot of people vote republican, yes, but it turns out that the states themselves are divided into smaller parts, i.e. counties, and then it looks like this, instead, sacred cow that is very red, I mean, just look this map is Isn't it obvious that the Republicans should have won?
Isn't it obvious that Mitt Romney had probably won the election? Just look at the amount of red stuff that took the election away from them. Have you heard that narrative before stopping this deal? So, okay, here's the question: should they have won right? That was the narrative that people were trying to push. I didn't say they won. I'm saying they should have won and that's all people need to hear well obviously they should have. obviously there is something wrong with the electoral gallows and we go to the streets and yes, there are many things wrong with the US electoral system, but no, I am not going to touch on the issue is that they do not vote. people do it and if you look at the number of voters by county or by state, we will see that there are many people living in California, as in California we have millions of people and in Wyoming we have millions of cattle they do many things but they do not vote well, some of them they could do it, but that's yeah, and here's the thing, we have the people and if we take that data and do the math again, we're going to find out that the Democrats actually won because the number of people who voted for the Democrats was a lot greater than the number of counties that did and do not vote, we have this tendency every time we look at a map to think that everything is the same if we have On a US map, for example, we tend to assign the same number to each area, so we look at a map and see a ton of red and think, hey, I think the Republicans won, that's a dangerous thing with maps, there's nothing wrong with that. maps is just the way we tend to interpret them, which means if you were to show a map you have to consider this we're staying in the US we're going to florida florida is a wonderful state it's warm enjoy the heat it's a little bit of wind sometimes that's part of the fun.
I guess they also passed a law in 2005, the so-called stand your ground law is a law that gives you the right to use deadly force if you're under attack. It's worse than that, it's completely okay for you to pull out a gun and shoot someone in the head if you feel threatened and it's also perfectly okay for you to shoot the same person in the head again, regardless of whether you could have de-escalated it. I just backed away, so they put this up. This is Reuters, kind of a big news outlet. They put this up to show what happened in 2005.
There are a few things that stand out here. When I saw this, I literally poured the coffee down my windpipe. because 721 is something less than 873, how the hell do we get this visual right? That's why they turned the damn thing around, they turned the visual around, this is what it should have looked like, what can we conclude from this? Well, when they enacted "Stand Your Ground," the number of fatal homicides, or technically it's not a homicide, it's just self-defense, skyrocketed. Who would have thought that this is not acceptable? This is an absolute lie from Reuters. I did some research and found out that someone asked the artist who made this image and asked them why they came up with this and the answer was good.
I wanted to show how blood flows. I'm going to call that one talking about, let's go to the UK again. The United Kingdom, since the beginning of the pandemic, they have been publishing the weekly Kobe 19 report. It has been identical since the beginning of the pandemic in 2019 and this is what the United Kingdom looked like, so we have a map, we have areas colored showing the Well, kovid prevalence is fine, so this was the case until week 40 of 2020. From week 40 onwards, they modified it slightly to call it weekly kobit 19 and seasonal flu report because it also They are adding seasonal flu. and as we can see in week 40, the UK finally managed to break the covid infection.
Everything was fine in the UK in week 40. Or was there no difference between the week 39 report and the week 40 report, apart from this glimpse? the scales changed the scales and therefore changed the color there is no information about this nothing how do people see this data? Well, they'll probably look at it and say, yeah, everything's a lot better. I'm going to buy things. or I'm going to go without a mask, God knows what you don't do, these people see what they want to see, we want this pandemic to end, believe me, I do and when I see something like this, apparently it's a normal thing, so um, how about where do we go from here?
Well, people have a limited attention span. People are not stupid. No, no, no, that's not what I'm saying, but people have a limited attention span. And just like I can choose who I ask and what I ask. It's practically a guarantee that you're going to draw a specific set of conclusions that make me with data infinitely more dangerous than someone who just yells and always be careful with maps because, like I said, we have a tendency to assign a map a specific number every time. different areas of the map have the same value, so to speak, that is something intrinsic to our minds, that is how we have learned to look at maps, that is, be very careful when working with colored maps, so-called visual choropleth maps . are powerful, most of us are predominantly visual, meaning we assign more impact to visual elements and again, how many people actually look at the data, we look at the visual, we correlate it with what we want to see, there we go, my goal was Show them how easy it is. it's to trick people with images and when I started developing this shoot, I had it in my head for a long time and I started developing it about two years ago and I didn't expect to find as many shenanigans as I did.
I mean, the things people do are widespread and even the established media occasionally makes mistakes and if you're tired of mistakes just go watch Fox, that's not a mistake, it's intentional and this was hilarious for a while. time because I realized how much garbage. It's out there and if these pretty basic things managed to fool so many people, what's out there that I didn't even see? I am in no way immune to this shitty information and images. I like to think it's harder to fool myself. That's what most people don't spend all day working with data, but I'm sure it will also be easy to fall, so the question is whether we are doomed to fail.
No I dont think so. I don't believe it unless you choose to be everyone has a choice we always have the choice to be better I can always be better tomorrow than I was today or yesterday that's a choice that's how I go through life I want to change I want to learn I think it is still possible to improve people basically help them think help them realize that things may not be as easy or simple as they seem we crave simplicity, unfortunately we can't have it and I think the solution is as obvious as the solution is rare It's called data literacy training, we automatically know very well how to correctly interpret red lights, we know not to buy things from strange men in alleys, we know not to eat yellow snow, these things are obvious, they are called common sense and It's common sense because we've been told this over and over again, what if we could make graphs and read graphs and read data with equal common sense?
What if we could teach this and this kind of critical stuff? Thinking about school, what if we could give our children basic knowledge like this in school? How difficult do you think it would be to fool them? There are many ways to do it. I highly recommend any of these books. Any of um alberto. cair is fantastic uh we have one of my favorites hans rusling uh he was taken away from us too soon. He had this fantastic way of making data accessible, making data relatable and no, he never made anything too difficult, it's both interesting and yet exciting, so don't play me if you start reading This and you fall into the very, very deep hole that is data visualization, it is a fantastic field, there are so many impressive people writing things, so much interesting information, so much understanding of the human being.
Condition and psychology that maybe you didn't expect when you went in there. We've seen examples of everything from fairly innocent mistakes to outright lies. So what can we conclude from this hour? Well, for starters, we can do it with great power. Sorry, I had to do it again. Making power bi carries a great responsibility, it is up to you who makes the visual to think about how this can be interpreted. I'm not going to give it to anyone who just created a visual and well, it's up to you to figure out what you're doing. looking no, it's the person creating the visual who has the responsibility to make sure it's as clear and concise as possible and we see what we want to see every day.
It's the same mechanism underneath racism, for example, we see what we want. I see as soon as something happens that fits a narrative we know well, we're just following that's the fact that supports my facts, something else comes along that doesn't fit what I know, what do I do, I dismiss it, apparently it's something strange this is how the mind works and I still think that increasing data literacy is key we can solve this it's not going to be easy but it can be done and it has to be done it has to be done I love you well let me rephrase that I challenge you to go out and help others to become more data literate.
You may not believe it, but this hour has earned you more points in the daily literacy column. Help others. Not everyone can access this, read a book or see it every time you hear something. strange data related explain things don't let it go we're not doomed yet not quite yet be observant be curious be literate and you won't get burned my name is

alexander

and thank you very much for this session

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