Placeholder Image

字幕表 動画を再生する

  • 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 theseeing is believingtrap

  • 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 frequentlyproblematic 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 moviesbased on a true storyreally

  • 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 -- thegood guys with gunstheory.

  • 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.

Hi I'm John Green.

字幕と単語

ワンタップで英和辞典検索 単語をクリックすると、意味が表示されます

B1 中級

データとインフォグラフィック。デジタル情報をナビゲートするクラッシュコース #8 (Data & Infographics: Crash Course Navigating Digital Information #8)

  • 5 0
    林宜悉 に公開 2021 年 01 月 14 日
動画の中の単語