シャドーイング練習: Statistical Thinking in Science: Crash Course Scientific Thinking #2 - 動画で英語スピーキングを学ぶ

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I am going to die, eventually.
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Which is pretty important to me personally, so I'd like to know roughly at what age I am most likely to die.
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You might guess something like 70, which, based on a national dataset,
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was the average age of death in the US for men who died between 2018 and 2023.
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But it might be that 79 is the more accurate answer, which is an extra 9 years.
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So how can I make sure I'm using the best number to answer my question?
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stats really tell me when I might die?
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And is there a way to look at these numbers and not have an existential crisis?
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Hi, I'm Hank Green, and this is Crash Course Scientific Thinking.
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Do not worry, I'm not going to teach you how to do statistics today.
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We have a whole other course about that.
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What we're talking about here is how to make sense of the stats you encounter in your everyday life.
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Statistics are vital for so much of what goes on around us, from designing video games to creating impactful government health policies.
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But statistics can be misleading.
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It's not because the numbers are lying.
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It's that if we don't understand how the numbers are being used, we might get the wrong impression about their meaning.
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Scientists use statistics to understand data, but when they're looking at those numbers, they have all of the context that goes along with them.
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By the time these stats are reported on in the news, they often lose some of that context.
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Which can have big impacts on the ways that we see the world as consumers of science news.
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Scientists rely on numbers to build knowledge.
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But since they can't measure every person, they use samples.
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Smaller groups they can measure to better understand a larger group.
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Which means there's always some uncertainty.
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So while stats could never tell me, Hank Green, exactly when I will die, they can tell me when a person like me is most likely to die.
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So what is the typical age of death for an American man?
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Well, when it comes to statistics, there's a few different ways of determining what's typical.
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One of the most common is to find the mean, or average, the sum of all the numbers in a sample divided by how many numbers are in that sample.
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That's where we get the first number from.
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Based on a large sample of residents who died between 2018 and 2023, the average or mean age of death of a man in the US is 70.
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But that mean is dragged down by people who died way younger than 70, even though there are fewer of them.
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So maybe I don't actually want the average.
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Maybe instead I want to know the most common age of death, or the mode.
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That answer is actually way different from the mean.
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The mode is the number that shows up the most in the data, which is where we get 79 from.
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But actually, most of the numbers in the sample are to the left of the mode, so it's actually more likely that I'd land on one of the numbers under 79 than
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that I'd land squarely on or after 79.
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So say then I want to find an age somewhat close to the average age when someone like me would die.
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I can look at the numbers in the graph and find the standard deviation, which tells me how spread out the other points in the sample are from the mean,
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which in turn can help me figure out how typical that number really is.
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If the standard deviation is small, that tells me most people in the sample are dying at ages pretty close to the average age.
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Another number that might be helpful is the median, or the point right in the middle of the group, where an equal number of US men die before and after.
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And that would be 73, still relatively close to 70 and 79, but different enough to matter.
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Because the median is always the number directly in the middle of the dataset, it is less likely to be skewed one way or the other the way a mean might be.
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So it might tell me way more about when American men tend to die.
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Though of course, it still cannot tell me when I'll die.
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The point is, averages like mean, median, and mode are different ways of telling you what might be typical.
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But they're way more useful when you understand how each one operates differently.
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And they're even more useful when combined with the standard deviation, which tells us how typical typical really is.
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There's always a degree of uncertainty when it comes to statistics.
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So another useful question is, okay, but how certain are we of these stats?
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For a stat to really mean anything, I need to know how much confidence to have in it.
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How likely is it that if I ran the numbers again, I'd get those same results?
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For that, I'd need to calculate a confidence interval, or a range of numbers that I can expect a result to fall within a certain percentage of the time.
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A 95% confidence interval means that if scientists repeated the study 100 times with new samples,
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the statistic they're measuring would fall in that range about 95 times.
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It shows how much that number might vary and how much trust can be put into it.
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A stat with a high confidence interval is quite predictive, but it is not perfect.
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So when encountering statistics in the real world, it's good to remember that every stat actually has two pieces.
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First, the number.
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And second, how precisely scientists know that number.
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And it is way better to be roughly right than precisely wrong.
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Hold on for a moment, I'm being told that we have a special guest on the way.
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It sounds like it's time for some sage advice.
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Hi, Hank.
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Did you know that women also die?
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Yes, I did sadly know that.
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Well, you should talk about dudes a lot.
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For example, consider this updated birth control pill.
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According to the news, it raised the risk of developing deadly blood clots by 100 percent!
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That's definitely a big statistic.
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It sounds like it, right?
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With the old pill, one in 7,000 people were at risk of developing blood clots.
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With the new pill, the risk doubled.
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Do you know what it became?
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Yeah, if it doubled, I guess it went from one to two.
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What a great guess!
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It sounds like a lot when someone says risk has increased by 100%, but that's just what scientists call the relative risk,
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or how much the likelihood of something happening gets bigger or smaller relative to something else.
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Which can be helpful to know, but it doesn't tell us the whole story.
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For that, we need the absolute risk, or the number of people actually experiencing an event in relation to the population at risk.
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The absolute risk stayed relatively low.
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Right?
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It increased to 2 in 7,000.
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Still important, but people need that context you talked about earlier to make informed decisions.
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At the time, though, a lot of people only learned about this risk in relative terms through the news.
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So people switched to less effective pregnancy prevention methods.
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And you know what poses a higher risk of life-threatening blood clots than the birth control pill?
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Pregnancy!
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So the more we understand numbers in context, the better we'll be at making informed decisions for our lives.
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And that's been today's Sage Advice.
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Thanks, Sage.
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Sage is correct.
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Understanding the difference between absolute risk and relative risk can help us make sense of
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so many of the stats we encounter in our daily lives.
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Like, how great is my risk of developing cancer if I go to the beach every day and don't wear sunscreen?
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Which actually brings me to my next point.
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Scientists often analyze relationships in data, like the relationship between sunscreen and skin cancer.
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These are known as correlations.
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A correlation is a relationship between two or more variables, which are basically anything that can be measured or counted.
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A correlation between two variables can be loose or it can be tight, which we quantify with their r-value.
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It's a number from negative one to one that shows how tightly two things move together.
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One means a perfect match, negative one means perfect opposite, and zero means no connection.
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The simplest kind of correlation is linear between just two variables.
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A correlation can be negative, meaning one variable gets smaller as the other gets bigger.
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Like, for example, how higher rates of wearing sunscreen correlate to lower rates of skin cancer.
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Or it can be positive, like if, say, higher rates of ice cream sales correlate to higher rates of shark attacks.
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You might have heard the saying, correlation doesn't equal causation.
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But there's actually more to it than that.
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Like in the case of sunscreen, there's a lot of good evidence that wearing it really does lower the risk of cancer.
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There is a causal link in the correlation.
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But in the case of shark attacks, it's safe to say that the ice cream isn't causing them.
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Warm weather is indirectly leading to both.
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In this case, weather is a confounding variable, or a factor that influences the outcome of a study without being controlled for.
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These can blur what's actually going on in the data if scientists don't measure and account for them.
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For example, some studies have shown a positive correlation between personal health and visits to the beach.
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But it's hard to know if beaches make people healthier, if healthy people are more likely to go to the beach, or if there's some third confounding variable,
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like the level of wealth, that results in both better health and more beach visits.
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And even if scientists do a good job of controlling for all these variables, they still have to ask, is it possible this result was just a fluke in our data?
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In other words, was it statistically significant?
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Statistical significance means the result is strong enough that it would be surprising to get by random chance.
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But don't let this phrasing mislead you either.
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In science, significant doesn't mean important.
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Like how I say, Doritos are a significant part of my life.
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That means they're important to me.
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But that's different from statistical significance.
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Total significance doesn't even necessarily mean meaningful in the real world.
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It's more like, it would be surprising to get this result at random, so we should dig deeper.
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And digging deeper is something we can all do when it comes to statistics.
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And that begins by understanding that there will always be some uncertainty.
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Scientists can't possibly measure every version of everything they want to study.
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But stats can help them measure the uncertainty.
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And understanding what numbers can and can't tell us about ourselves, each other, and the world can help us not only better understand the way that science works,
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but also help us make more informed judgments about our own lives. In our next episode,
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we're going to look at how rare it actually is for a single experiment to change our understanding of science.
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I'll see you then.
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This episode of Crash Course Scientific Thinking was produced in partnership with HHMI Bio interactive, bringing real science stories to thousands of high school and undergrad life science classrooms.
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If you're a teacher, visit their website for resources that explore the topics we discussed in this video today.
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Thank you.

この動画が英語スピーキング練習に最適な理由

科学的思考を話す際の自然な語調やリズムが完璧に表れているこの動画は、shadowspeak(シャドースピーチ)の理想的な教材です。Hank Greenの話し方は明瞭で、日常的な会話に近いペースでありながら、専門用語も適度に含まれているため、IELTS スピーキング対策にも役立ちます。特に「統計の意味を正しく理解する」というテーマは、論理的な話し方を鍛えるのに最適で、発音やイントネーションの練習にも最適です。

自然な表現を分解してみよう

  • 「But it might be that 79 is the more accurate answer, which is an extra 9 years.」:「might be that」の柔らかい仮定表現と「which is」による情報の追加が自然で、日常会話でよく使われる構造です。発音では「extra」の「e」を軽く発音することで、流れがスムーズになります。
  • 「Statistics are vital for so much of what goes on around us, from designing video games to creating impactful government health policies.」:「from...to...」で範囲を広げる表現が効果的で、「vital for」や「impactful」などの語彙が論理的な話し方を引き立てます。「goes on around us」のリズムを真似ることで、英語の自然な流れが身につきます。
  • 「The point is, averages like mean, median, and mode are different ways of telling you what might be typical.」:「The point is」で要点をまとめる表現は、話し手の意図を明確にするのに重要です。「different ways of telling you」のような柔らかい表現は、相手に優しく情報を伝える技術です。

簡単な練習ルーティンで英語の発音を良くする

シャドースピーチの基本は「リピート&レコード」です。まず動画を10秒ずつ再生し、すぐにその後に自分の声で真似てみましょう。特にHank Greenの語尾の上げ下げやポーズを注意深く聞き、同じリズムで話すことがポイントです。録音した自分の声を動画と比べて、「extra」の発音や「The point is」のイントネーションの違いを確認しましょう。1日10分、1週間続けるだけで、自然な英語の流れが身につき、IELTS スピーキング対策にも役立つはずです。shadowspeakの魔法を信じて、毎日少しずつ進歩していきましょう!

この動画の文法

話し手がよく使っている文型を、動画の実際の表現とともに紹介します。

文型動画での表現
受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てるare reported · is dragged · be skewed
関係詞節 who / which + 節 — 人や物について情報を加えるmen who died · answer, which is · people who died
現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係しているhas increased · have shown

シャドーイングとは?英語上達に効果的な理由

シャドーイング(Shadowing)は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的:ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影(shadow)」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →