Hi I’m John Green. This is Crash Course: Navigating Digital Information. So what would you say if I told you that 90%
of people polled say that they love Crash Course and think we offer consistently reliable
and accurate information on the most important educational topics. You might say, “Hold on. I’ve seen the comments. That can’t be true.” And you’d be kind of right, but I would
also be kind of right, because I did do that survey, and 90% of people did agree with those
positive statements about Crash Course–but I surveyed 10 people who work on Crash Course. It would’ve been 100%, but Stan said, “Is
this for a bit? I’m not participating.” Anyway, whether it’s 4 out of 5 dentists
or 9 out of 10 crash course viewers, source and context can make all the difference. We like to think of data as just being cold,
hard facts, but as we’ve already learned in this series, there is no single magical
way to get at the singular truth. We have to place everything in its context–even
statistics. In fact, especially statistics. INTRO Okay, so data is raw quantitative or qualitative
information, like facts and figures, survey results, or even conversations. Data can be derived from observation, experimentation,
investigation or all three. It provides detailed and descriptive information
about the world around us. The number of teens who use Snapchat, the
rate at which millennials move in or out of a neighborhood, the average temperature of
your living room — those are all data points. And data is a really powerful form of evidence
because it can be absorbed quickly and easily. Like we often consume it as numbers, like
statistics, or as visual representations, like charts and infographics. But as Mark Twain once famously noted: “There
are three kinds of lies. Lies, damned lies, and statistics.” Statistics can be extraordinarily helpful
for understanding the world around us, but because statistics can seem neutral and irrefutable,
they can be used to profoundly deceive us as well. The truth is neither data nor interpretations
of it, are neutral. Humans gather, interpret, and present data
and we are flawed, complex, and decidedly unneutral. Unfortunately, we often take data at face
value. Just like with photos and videos, we can get
stuck in the “seeing is believing” trap because we don’t all have the know-how to
critically evaluate statistics and charts. Like a Stanford History Education Group study
from 2015 bears this out. SHEG, developed the MediaWise curriculum that
this series is based on. And they asked 201 middle schoolers to look
at this comment on a news article. As you can see, the comment includes healthcare
statistics, but doesn’t say where they came from. It doesn’t provide any biographical information
on the commenter either. But, 40% of the students indicated they’d
use that data in a research paper. In fact many cited the statistics as the reason
they found the comment credible and useful. The sheer existence of quote unquote data
enhanced its credibility despite there being no real reason to trust that data. Whenever we come across data in the wild,
we should ask ourselves two questions: Does this data actually support the claim
being made? And is the source of this data reliable? Here’s an example when it comes to data
relevance. At the 2018 U.S. Open, Serena Williams was
penalized for yelling at the umpire and smashing her racket during the game. On the court, she argued that men yell far
worse things at umpires and physically express their emotions all the time without being
penalized and a few weeks later, journalist Glenn Greenwald cited a New York Times story
in a tweet: “Now, NYT just released a study of the actual
data: contrary to that narrative, male tennis players are punished at far greater rates
for misbehavior, especially the ones relevant to that controversy: verbal abuse, obscenity,
and unsportsmanlike conduct” Well that sounds very authoritative. And also he linked to a table that showed
that far more men have been fined for racket throwing and verbal abuse than women during
grand slam tournaments. However, as statistician Nate Silver helpfully
pointed out, this stat only shows that men are /punished/ more, which could be because
they misbehave more. So all these statistics actually show is the
raw number of punishments, not the rate of punishment despite Greenwald’s claims. To get the rate of punishment we’d have
to divide the number of punishments by how many times men and women misbehave, and that
data isn’t provided here. So the data in the end does not support Greenwald’s
tweet at all, making his claim that male tennis players are punished more frequently… problematic
at best. To be fair Serena Williams claim is also anecdotal,
although, you know she does watch a lot of tennis. We also need to investigate whether the source
providing the data is reliable, and we can do that through lateral reading. That means opening new tabs to learn more
from other sources about: who commissioned the research behind data
, who conducted the research, and why We also need to know if the source of the
the information is authoritative, or in a good position to gather that data in the first
place. Like remember in episode 3 of this series
when we talked about the claim that Americans use 500 million straws per day? We couldn’t confirm how many straws Americans
actually use every day, but we did see that sources across the web cited that statistic
even though we found out that it came from a 2011 report written by a then-nine year
old child, Milo Cress. To come up with the figure, he called up straw
manufacturers to ask how many straws they made. There’s no way of knowing if those manufacturers
were telling the truth, or if the group he called is representative of the whole industry. He was 9. He was obviously a very bright and industrious
9 year old, but he was 9! Apologies to all the 9 yr olds watching. Thank you for being careful in how you navigate
digital information friends. A more reliable source of such far-reaching
information might be a nonpartisan research organization like the Pew Research Center. They’re known for reliable, large-scale
studies on U.S. trends and demographics. Once we know who a source of data is, whether
they’re authoritative, and why they gathered it, we should ask ourselves what perspective
that source may have. They could have a vested interest in the results. Like the beauty influencer you follow who’s
always saying 92% of users of this snail slime facial get glowing skin in 10 days. That study may be accurate but there also
may be a hashtag-ad in the caption to quietly let you know that the brand in question is
paying them. But forget about snail slime. Have I told you about Squarespace? We have to take into account when people cite
data that helps them make money. Including me. Alright, so once we know more about where
our data comes from, it’s time to analyze how it’s presented. Data visualizations, like charts and graphs
and infographics, can be amazing ways of displaying information because one they’re fun to look
at, and two the best infographics take complex subjects and abstract ideas and turn them
into something that we understand. Like I love this one that shows how factual
movies “based on a true story” really are. Oh, and this one on cognitive biases. Although I might be cognitively biased towards
appreciating a graphic about cognitive biases. The great thing about data visualization is
that it’s a creative field, limited only by a designer’s imagination. But of course with artistic license comes
the ability to present data in ways that sacrifice accuracy. It’s really quite easy to invent a nice-looking
graphic that says whatever you want it to say. So we need to read them carefully and make
sure there’s actually data behind a data visualization. For instance, look at this chart. It makes a claim that, when guns are legal,
lives are saved because gun owners prevent deadly crimes — the “good guys with guns”
theory. But if you read the fine print, the chart
acknowledges that statistics are not kept on crime /prevention/, or crimes that never
happened — so these figures are not based on real data at all. The chart also says that fewer homicides take
place when guns are legal than when they’re banned. But what it doesn’t say is where this change
would supposedly take place, and over what span of time. For instance homicides went down in Australia
after strict gun control legislation was passed on the other hand they also went down in the
United States as gun ownership increased. What is clear upon closer inspection is that
this graphic, which initially appears to have some pretty dramatic estimates about gun control,
is by its own admission mostly speculation. To trust a data visualizations we need to
make sure that it is based on real data AND that the data is presented fairly. Let’s go to the Thought Bubble. Here’s a graph that was posted to Twitter
by The National Review, a conservative site that often denies the effects of climate change. It uses data from NASA on the average global
temperature from 1880 to 2015. It looks like a nearly straight line, with
only a slight increase at the end and the tweet, “the only #climatechange chart you
need to see” implies that it once and for all shows that
the climate isn’t really getting warmer. However, the y-axis of this chart shows -10
to 110 degrees, which makes the scale of this data very small. One might say that the chart misleads by zooming
out too far. If, for instance, the scale was truncated
to show just 55 to 60 degrees, as in this Washington Post graphic using the same data,
the change over time looks much more dramatic. And the original post also leaves out some
much needed context. The entire globe shifting its average temperature
by even a couple degrees over the period shown is extremely unusual
and has an outsized impact on how the climate functions. The first chart does not present the change
in this data or its significance in good faith. On the other hand, data visualization can
also be very misleading if it zooms in too much. this chart produced by the administration
of President Barack Obama shows how a truncated y-axis can /create/ manipulation, not solve
it. The data behind this chart on graduation rates
is reliable, but by zooming in the scale to show from around 70 to 85%, it makes the change
throughout Obama’s administration look much more dramatic. Here’s what it would look like if you could
see the entire scale. The increase in graduation rates looks much
less significant. This follows the proportional ink principle
of data visualization. The size of a filled in or inked area should
be proportional to the data value it represents. Thanks, Thought Bubble. So a few simple tweaks to how data is presented
can really make a big difference in how it’s interpreted. Whenever we encounter data visualizations,
we need to check that the data is accurate and relevant, that its source is reliable,
and that the information is being presented in a way that is honest about the conclusions
it draws. Actually, once you get the hang of sorting
the useful, well-designed data visualizations from poorly designed ones, the bad ones can
be pretty entertaining. If you’d like to see some exceptionally
terrible charts, take a spin through viz.wtf or the subreddit data is ugly. I especially fond of this completely indecipherable
chart about the Now That’s What I Call Music CDzs, courtesy of the BBC. The challenge and opportunity of images is
that they are so eye-catching that we sometimes forget that they’re created by and for humans
who have the ability to manipulate them for their own ends. To make our information of lower quality and
thereby make our decisions of lower quality. And the use of infographics and big data have
become even more popular as our attention spans have waned. After all, it’s much easier to read a pie
chart than an essay or an academic report. Plus it fits into a tweet. In summary, whether you’re encountering
raw data on its own or visual representations of it, it’s very important to keep a critical
eye out for reliability and misrepresentation. Thank you for spending several minutes of
your waning attention with us we’re going to get deeper into that next time
I’ll see you then.

100 thoughts on “Data & Infographics: Crash Course Navigating Digital Information #8”

  1. About the intro (0:03 – 0:36): STAN IS THE MAN! All praise Stan!

    (And I loled at "I've seen the comments. That can't be true.")

  2. That so called data on men beings penalized for bad behavior is so flawed it makes me question the basis on which you make you make your assumptions. You are definitely not considering the facts, but obviously showing your bias.

  3. 6:39 "Forget about snail slime. Have I told you about Squarespace?" You evil man! I was just starting to take a drink of water!

  4. Talks about nine year olds, than talks about pew research.
    Is John hinting that he supports pewdiepie?
    Could be a coincidence.

  5. Personally, I'd like more charts that were like 1 in x are blank. Like for the Obama chart it'd go from 1 in 4 not graduating high school to 1 in 5.xx. It's basically a "race to the nines" style of framing the statistics.

  6. This is an excellent, and needed, addition to all of the Crash Course modules. I wonder if these guys ever saw the Penn & Teller BS show….

  7. In my early 20s, I was a data manager on two NH-funded studies. It opened my eyes to how we can be manipulated and how we manipulate others. I was never able to see data presented in ads or articles or memes the same way ever again. This should be taught in high schools.

  8. Love this! Would really appreciate an episode where you outline all the best banks of reliable accurate data we can find, rather than show us how we can navigate through potentially bad sources.

  9. As a graphic designer, this episode is going to help me be much more aware of potentially misleading messages in the infographics I create. I wish that this topic had been present in the (albeit basic) infographics training I received – especially considering how much can apparently be covered in 13 minutes! Thank you for another great episode.

  10. Learning to interpret data should be required in public schools, especially in the digital age. It needs to go hand-in-hand with teaching critical thinking. Unfortunately, often in the US critical thinking isn't just ignored, but viewed with hostility.

  11. I can hardly wait for the whole series to be available as a playlist so I can send it to all the college educated adults who believe the junk they see online.

  12. Almost comically given the topic, critical review begs the question, do you have data justifying your assertions that; “THE ENTIRE GLOBE SHIFTING ITS AVERAGE TEMPERATURE BY EVEN A COUPLE OF DEGREES OVER THE PERIOD SHOWN IS EXTREMELY UNUSUAL”, and, “AND HAS AN OUTSIZED IMPACT ON HOW THE CLIMATE FUNCTIONS.” (capitals and bold print your choice)? That would be A LOT OF DATA (capitals and bold my choice). Further, your opinion that “the original (National Review) post leaves out some much-needed context” is precisely the burr when working to comb the factual from the counter-factual; in a rational scheme, the data forms the basis for the context, not the inverse. I will assume that you were exercising satirical license with this poorly conceived example and refrain from labeling you with the H-word moniker.

  13. 10:28 "The size of a filled in or inked area should be proportional to the data value it represents." The way I interpret that is that a 1% movement in the graph should represent a 1% movement in the data. How do you determine what 100% is? Should all temperature graphs start at 0 Kelvin? Should all graphs representing percentages range from 0% to 100% even when it is completely implausible that the exreme values could ever be present in the data? Instead, I would be inclined to consider only the plausible range of values (so, probably not including 0 Kelvin) — and plausible is a judgement call.

  14. Just saying, if you're hoping to educate viewers from across the political sphere, picking contentious examples is likely not the best way to do it. Maybe teach the techniques and let them come to conclusions by themselves? More convincing that way.

  15. @5:50 there was a reference to pewdipie fans, pewdiepie is a largre youtuber with the hight 173cm, he has been getting attacked by T-series and wall street journal but he has a 9 year old army

  16. Why should dumb people vote? I mean, let's take the average intelligence. Why people with less than average intelligence vote? What is there to gain? What would we lose as a society if the morons don't vote?

  17. Now apply this knowledge to Climate Change statistics and see if you can spot the hoax. I give you a hint, look at the vertical axis of the graph that shows how temperature rose over the years. How many degrees are there?

  18. Statistics are often used to knock down straw-men. But you have to understand straw-men to know if that's happening.

  19. I would highly recommend the classic "How to Lie with Statistics" by Darrell Huff to anyone interested in a better understanding of this topic

  20. The worst thing about this series is that the people who need to watch this the most are the ones who want to watch this the least.

  21. I was checking out the polls during the last election and was surprised to see in small print at the bottom of the polls the actual number of people polled. It was always below 200, and this was supposed to reflect the entire country! Yikes, really changed how I look at polls

  22. World going to kitter? No worries, just watch John and friends at CrashCourse for your daily dosage of common sense. This course in particular is so on topic. Big thanks!

  23. This series is excellent. This really should be taught in schools (and also to a lot of adults on my facebook!) Hats off to the animation/production team too, these vids look great.

  24. About data visualization manipulation on the graduation rate statistics at 9:50. Showing scale from 0% to 100% might not be the most “correct” way either. Consider a counterfactual example: in 2009 it climbs from 80% to 90%, but in 2010 it climbs from 90% to 100%. In the 0%–100% scale it would look like a similar climb, but it would be much much more difficult to get from 90 to 100 percent than from 80 to 90 percent. I have no idea how to present such date correctly. Logarithmic scale is not good either.

  25. The underlying power of statistics is that they allow us to make quantitative predictions and then test very precisely how good those predictions were and how confident we are about that and this is often much more precise than when describe something qualitatively.

  26. Relevant to this video: although it's quite old, the book How to Lie With Statistics is a great book about this topic.

    (It was written in the 1950s, though, so be aware you ought to multiply any dollar amount by about 10 to get its value in 2019 dollars.)

  27. while i agree that seeing the entire scale of the chart is important, it could be misleading, too. Sometime there is a line in your progress that, once you cross, it will be significantly harder to improve. Like, Germany economy grew 1,5% in 2018, while Vietnam – a middle lower income, developing country – grew 7,08% in the same year. If you put that in a "fair" chart, Germany will seem to be outperformed by Vietnam, while we all know it's the opposite. Another example can be found in videogame: You could easily go from level 1 to level 10 in the matter of days, but level 80 to 81 will require months of grinding. It's doesn't mean that level 80 player are worse than a newbie; he just have to deal with a much higher requirement to advance, espescially when compare with new players.

  28. How do I determine a source is reliable? I understand that all of these techniques are very important in the wilderness of the internet, especially social media, and should be used, but I just can't afford the time it takes to evaluate every single news article I read from a place I trust

  29. Just for the record, I love crash course and think that you provide correct information on many beneficial and educational topics. 🙂

  30. You mention a fair number of interesting or entertaining websites in this video. I wish the video description contained links to these sites.

  31. I dont understand Serena Williams case. She yelled, smashed her racket and got penalized. Then she claimed men did not get penalized even when they do worse. Statistics show men do get punished. Yes, it doesn't tell the rates of penalization of men and women, but it also doesn't prove the rates women get punished is higher. This looks more like Serena Williams did something wrong, and made the issue about sexism to get away with it. Lets say she is right about men getting less punishment, but does it make what she did ok? That is the real question.

  32. 6:00 ~ Pew is a Nightmare. Way more graphics or headline producing, yet Research Gate is doing a much better job. ~ Tainted Judgment before I start reading, all the time listening for the Wealth=Christianity data, and sometimes like GDP of nations, you bastards must include debt!

    No French Ballooning Stupid into authority over Other Nations. (Original art of first hot air balloon. How do you land that fly ash hot lid?)

  33. You shouldn't make an argument from what you don't know (an argument from ignorance, this is a fallacy).

  34. "There are only three kinds of lies: Lies, Damn Lies, and Statistics" – Mark Twain

    (Yes I know he probably never said that.)

  35. I the last Chilean elections, the candidate that won used many charts purposely made to mislead, but people only noticed when they made a bar chart were the smaller data set was the longer bar, so people began retrospectively question his data. He still won though.

  36. So the source of your Glasses-Dealer in which he recommended you these glasses wasn't correct but you didn't check… nice tutor

  37. You remind me of Daniel Jackson/Michael Shanks from SG-1 back in the day.

    I love your series.. but I've noticed a change in you recently. Hope all is well with you man. take care

  38. I'm glad to see this topic all over the internet, thank you. The problem we have is that big companies and politicians have psychologists working for them. Yes, psychologists working for marketing agencies who are specialist in manipulation. It's quite hard to fight against them, but videos like this can help. I don't know how those people can sleep knowing they are lying

  39. I just wanted to thank you for using the Mark Twain quote. I would have been disappointed if you had left it out.

  40. The bottom line is that Serena tried to play the gender card and she not only got fact checked but roundly criticized for being a spoiled tennis brat. She ruined the tournament.

    The most you could argue was that the data was inconclusive not that Greenwald was disproven. But either way the fact remains that men got punished not just women and Serena is a cry bully.

Leave a Reply

Your email address will not be published. Required fields are marked *