Luyện nói tiếng Anh bằng Shadowing qua video: Statistical Thinking in Science: Crash Course Scientific Thinking #2
Đang tạo bài học...
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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.
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Từ vựng và ghi chú luyện nói cho bài học này
Video này có 138 câu và 1934 từ để luyện shadowing. Phần lời nói dài 11:01. Người nói nói với tốc độ tự nhiên, khoảng 175 từ mỗi phút, gần với hội thoại hằng ngày. 87% số từ nằm trong 3.000 từ tiếng Anh thông dụng nhất; phần còn lại nên xem trước khi luyện.
Từ vựng quan trọng trong video
15 từ đáng học trong video, kèm phiên âm và nghĩa:
| Từ | Phiên âm | Nghĩa |
|---|---|---|
| scientist danh từ | /ˈsaɪəntəst/ | nhà khoa học, khoa học gia |
| relative danh từ | /ˈɹɛl.ə.tɪv/ | họ hàng, người thân |
| pill danh từ | /pɪl/ | viên, viên thuốc |
| sage danh từ | /seɪd͡ʒ/ | xô thơm, xôn |
| encounter động từ | /ɪnˈkaʊntɚ/ | bắt gặp |
| significance danh từ | /sɪɡˈnɪfɪkəns/ | ý nghĩa |
| inform động từ | /ɪnˈfɔɹm/ | thông báo |
| median danh từ | /ˈmiː.di.ən/ | trung tuyến |
| interval danh từ | /ˈɪntɚvəl/ | khoảng thời gian |
| helpful tính từ | /ˈhɛlp.fl̩/ | hay giúp đỡ |
| pregnancy danh từ | /ˈpɹɛɡnənsi/ | sự có thai |
| topic danh từ | /ˈtɑpɪk/ | chủ đề |
| partnership danh từ | /ˈpɑɹtnɚʃɪp/ | công ty hợp danh |
| percentage danh từ | /pə(ɹ)ˈsɛntɪd͡ʒ/ | số phần trăm, tỷ lệ phần trăm |
| everyday tính từ | /ˈɛvɹiˌdeɪ/ | hằng ngày, thường ngày |
Phát âm cần chú ý
Người nói dùng 33 dạng rút gọn, ví dụ don't, I'd, they're. Hãy nói theo dạng ngắn đúng như bạn nghe.
- Âm “sh” và “zh”: correlation /ˌkɔɹ.əˈleɪ.ʃən/, partnership /ˈpɑɹtnɚʃɪp/, impression /ɪmˈpɹɛʃn̩/, relation /ɹɪˈleɪʃ(ə)n/, prevention /pɹɪˈvɛnʃən/
- Từ dài — đặt trọng âm cho đúng: correlation /ˌkɔɹ.əˈleɪ.ʃən/, statistical /stəˈtɪs.tɪ.kəl/, uncertainty /ʌnˈsɜːtənti/, significance /sɪɡˈnɪfɪkəns/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/
Cách luyện với video này
- Nghe hết video một lần, chưa cần nói, và ghi lại những từ bạn chưa biết.
- Bắt đầu ở tốc độ 0,75×, nói đuổi từng câu, rồi quay lại tốc độ bình thường khi đã quen.
- Ghi âm giọng mình rồi so với bản gốc, chú ý các từ như scientist, relative, pill.
Ngữ pháp trong video
Những cấu trúc người nói dùng nhiều nhất, kèm đúng cụm từ trong video:
| Cấu trúc | Trong video |
|---|---|
| Câu bị động be + quá khứ phân từ — nhấn vào việc xảy ra, không phải người làm | are reported · is dragged · be skewed |
| Mệnh đề quan hệ who / which + mệnh đề — thêm thông tin về người hoặc vật | men who died · answer, which is · people who died |
| Thì hiện tại hoàn thành have/has + quá khứ phân từ — việc đã xảy ra nhưng còn liên quan đến hiện tại | has increased · have shown |
Phương Pháp Shadowing Là Gì?
Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.