Shadowing Practice: Statistical Thinking in Science: Crash Course Scientific Thinking #2 - Learn English Speaking with Video
Creando lección...
1
I am going to die, eventually.
2
Which is pretty important to me personally, so I'd like to know roughly at what age I am most likely to die.
3
You might guess something like 70, which, based on a national dataset,
4
was the average age of death in the US for men who died between 2018 and 2023.
5
But it might be that 79 is the more accurate answer, which is an extra 9 years.
6
So how can I make sure I'm using the best number to answer my question?
7
stats really tell me when I might die?
8
And is there a way to look at these numbers and not have an existential crisis?
9
Hi, I'm Hank Green, and this is Crash Course Scientific Thinking.
10
Do not worry, I'm not going to teach you how to do statistics today.
11
We have a whole other course about that.
12
What we're talking about here is how to make sense of the stats you encounter in your everyday life.
13
Statistics are vital for so much of what goes on around us, from designing video games to creating impactful government health policies.
14
But statistics can be misleading.
15
It's not because the numbers are lying.
16
It's that if we don't understand how the numbers are being used, we might get the wrong impression about their meaning.
17
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.
18
By the time these stats are reported on in the news, they often lose some of that context.
19
Which can have big impacts on the ways that we see the world as consumers of science news.
20
Scientists rely on numbers to build knowledge.
21
But since they can't measure every person, they use samples.
22
Smaller groups they can measure to better understand a larger group.
23
Which means there's always some uncertainty.
24
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.
25
So what is the typical age of death for an American man?
26
Well, when it comes to statistics, there's a few different ways of determining what's typical.
27
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.
28
That's where we get the first number from.
29
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.
30
But that mean is dragged down by people who died way younger than 70, even though there are fewer of them.
31
So maybe I don't actually want the average.
32
Maybe instead I want to know the most common age of death, or the mode.
33
That answer is actually way different from the mean.
34
The mode is the number that shows up the most in the data, which is where we get 79 from.
35
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
36
that I'd land squarely on or after 79.
37
So say then I want to find an age somewhat close to the average age when someone like me would die.
38
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,
39
which in turn can help me figure out how typical that number really is.
40
If the standard deviation is small, that tells me most people in the sample are dying at ages pretty close to the average age.
41
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.
42
And that would be 73, still relatively close to 70 and 79, but different enough to matter.
43
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.
44
So it might tell me way more about when American men tend to die.
45
Though of course, it still cannot tell me when I'll die.
46
The point is, averages like mean, median, and mode are different ways of telling you what might be typical.
47
But they're way more useful when you understand how each one operates differently.
48
And they're even more useful when combined with the standard deviation, which tells us how typical typical really is.
49
There's always a degree of uncertainty when it comes to statistics.
50
So another useful question is, okay, but how certain are we of these stats?
51
For a stat to really mean anything, I need to know how much confidence to have in it.
52
How likely is it that if I ran the numbers again, I'd get those same results?
53
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.
54
A 95% confidence interval means that if scientists repeated the study 100 times with new samples,
55
the statistic they're measuring would fall in that range about 95 times.
56
It shows how much that number might vary and how much trust can be put into it.
57
A stat with a high confidence interval is quite predictive, but it is not perfect.
58
So when encountering statistics in the real world, it's good to remember that every stat actually has two pieces.
59
First, the number.
60
And second, how precisely scientists know that number.
61
And it is way better to be roughly right than precisely wrong.
62
Hold on for a moment, I'm being told that we have a special guest on the way.
63
It sounds like it's time for some sage advice.
64
Hi, Hank.
65
Did you know that women also die?
66
Yes, I did sadly know that.
67
Well, you should talk about dudes a lot.
68
For example, consider this updated birth control pill.
69
According to the news, it raised the risk of developing deadly blood clots by 100 percent!
70
That's definitely a big statistic.
71
It sounds like it, right?
72
With the old pill, one in 7,000 people were at risk of developing blood clots.
73
With the new pill, the risk doubled.
74
Do you know what it became?
75
Yeah, if it doubled, I guess it went from one to two.
76
What a great guess!
77
It sounds like a lot when someone says risk has increased by 100%, but that's just what scientists call the relative risk,
78
or how much the likelihood of something happening gets bigger or smaller relative to something else.
79
Which can be helpful to know, but it doesn't tell us the whole story.
80
For that, we need the absolute risk, or the number of people actually experiencing an event in relation to the population at risk.
81
The absolute risk stayed relatively low.
82
Right?
83
It increased to 2 in 7,000.
84
Still important, but people need that context you talked about earlier to make informed decisions.
85
At the time, though, a lot of people only learned about this risk in relative terms through the news.
86
So people switched to less effective pregnancy prevention methods.
87
And you know what poses a higher risk of life-threatening blood clots than the birth control pill?
88
Pregnancy!
89
So the more we understand numbers in context, the better we'll be at making informed decisions for our lives.
90
And that's been today's Sage Advice.
91
Thanks, Sage.
92
Sage is correct.
93
Understanding the difference between absolute risk and relative risk can help us make sense of
94
so many of the stats we encounter in our daily lives.
95
Like, how great is my risk of developing cancer if I go to the beach every day and don't wear sunscreen?
96
Which actually brings me to my next point.
97
Scientists often analyze relationships in data, like the relationship between sunscreen and skin cancer.
98
These are known as correlations.
99
A correlation is a relationship between two or more variables, which are basically anything that can be measured or counted.
100
A correlation between two variables can be loose or it can be tight, which we quantify with their r-value.
101
It's a number from negative one to one that shows how tightly two things move together.
102
One means a perfect match, negative one means perfect opposite, and zero means no connection.
103
The simplest kind of correlation is linear between just two variables.
104
A correlation can be negative, meaning one variable gets smaller as the other gets bigger.
105
Like, for example, how higher rates of wearing sunscreen correlate to lower rates of skin cancer.
106
Or it can be positive, like if, say, higher rates of ice cream sales correlate to higher rates of shark attacks.
107
You might have heard the saying, correlation doesn't equal causation.
108
But there's actually more to it than that.
109
Like in the case of sunscreen, there's a lot of good evidence that wearing it really does lower the risk of cancer.
110
There is a causal link in the correlation.
111
But in the case of shark attacks, it's safe to say that the ice cream isn't causing them.
112
Warm weather is indirectly leading to both.
113
In this case, weather is a confounding variable, or a factor that influences the outcome of a study without being controlled for.
114
These can blur what's actually going on in the data if scientists don't measure and account for them.
115
For example, some studies have shown a positive correlation between personal health and visits to the beach.
116
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,
117
like the level of wealth, that results in both better health and more beach visits.
118
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?
119
In other words, was it statistically significant?
120
Statistical significance means the result is strong enough that it would be surprising to get by random chance.
121
But don't let this phrasing mislead you either.
122
In science, significant doesn't mean important.
123
Like how I say, Doritos are a significant part of my life.
124
That means they're important to me.
125
But that's different from statistical significance.
126
Total significance doesn't even necessarily mean meaningful in the real world.
127
It's more like, it would be surprising to get this result at random, so we should dig deeper.
128
And digging deeper is something we can all do when it comes to statistics.
129
And that begins by understanding that there will always be some uncertainty.
130
Scientists can't possibly measure every version of everything they want to study.
131
But stats can help them measure the uncertainty.
132
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,
133
but also help us make more informed judgments about our own lives. In our next episode,
134
we're going to look at how rare it actually is for a single experiment to change our understanding of science.
135
I'll see you then.
136
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.
137
If you're a teacher, visit their website for resources that explore the topics we discussed in this video today.
138
Thank you.
📺 Mismo canal
C11
Vectors and 2D Motion: Crash Course Physics #4
TOEFL PrepCambridge Exam PrepUniversity Students
C11
How Often do Asteroids Hit the Earth?: Crash Course Geology #20
IELTS PrepTOEFL PrepUniversity Students
C1What's all the Yellen About? Monetary Policy and the Federal Reserve: Crash Course Economics #10
IELTS PrepBusiness EnglishUniversity Students
C12
Unit Conversion & Significant Figures: Crash Course Chemistry #2
Business EnglishUniversity StudentsWorking Adults
C11
What Is Myth? Crash Course World Mythology #1
Cambridge Exam PrepUniversity Students
B25
Mesopotamia: Crash Course World History #3
Cambridge Exam PrepHigh School StudentsUniversity Students
B210
Crash Course Sex Ed Preview
Daily ConversationUniversity StudentsWorking Adults
C110
The Agricultural Revolution: Crash Course World History #1
IELTS PrepTOEFL PrepCambridge Exam Prep
C12
Derivatives: Crash Course Physics #2
Cambridge Exam PrepUniversity StudentsWorking Adults
B23
Indus Valley Civilization: Crash Course World History #2
Cambridge Exam PrepHigh School StudentsUniversity Students
B22
Ancient Egypt: Crash Course World History #4
Cambridge Exam PrepHigh School StudentsUniversity Students
C12
What is Engineering?: Crash Course Engineering #1
Business EnglishUniversity StudentsWorking Adults
B22
The Persians & Greeks: Crash Course World History #5
Cambridge Exam PrepHigh School StudentsUniversity Students
C12
Supervised Learning: Crash Course AI #2
Business EnglishUniversity StudentsWorking Adults
C13
The Periodic Table: Crash Course Chemistry #4
IELTS PrepTOEFL PrepCambridge Exam Prep
B22
Where Do Diamonds Come From?: Crash Course Geology #8
IELTS PrepTOEFL PrepCambridge Exam Prep
B29
Why You Need Trust to Do Business: Crash Course Business - Soft Skills #1
Business EnglishUniversity StudentsWorking Adults
C13
Where Did Democracy Come From, and Does It Still Work?
IELTS PrepTOEFL PrepUniversity Students
C12
The Nucleus: Crash Course Chemistry #1
IELTS PrepTOEFL PrepUniversity Students
B21
Defense Against the Dark Arts of Influence: Crash Course Business Soft Skills #2
Business EnglishUniversity StudentsWorking Adults
✨ Recomendar Video
C12
582 oraciones
You've (Likely) Been Playing The Game Of Life Wrong
Business EnglishUniversity StudentsWorking Adults
C11
402 oraciones
The Future of Data Science - Data Science @ Stanford
Business EnglishUniversity StudentsWorking Adults
C11
125 oraciones
Introduction to statistical prediction models
Business EnglishUniversity StudentsWorking Adults
C11
710 oraciones
The truth about peptides, NAD, & other longevity treatments | Rachele Pojednic, Ph.D.
Business EnglishUniversity StudentsWorking Adults
C19
61 oraciones
Keep your brain from declining after age 30
Business EnglishUniversity StudentsWorking Adults
C12
224 oraciones
What is Statistics? A Beginner's Guide to Statistics (Data Analytics)!
Business EnglishUniversity StudentsWorking Adults
C1262 oraciones
Don't choose the wrong tech career in 2026
Business EnglishUniversity StudentsWorking Adults
C186 oraciones
The End of Human Mathematics? AI's Recent Breakthrough -- And What's Next
Business EnglishUniversity StudentsWorking Adults
C15
142 oraciones
Evaluating Sources & Fact Checking: Crash Course Scientific Thinking #6
Business EnglishUniversity StudentsWorking Adults
B25
37 oraciones
Steve Jobs Famous 2005 commencement speech at Stanford University
Business EnglishDaily ConversationUniversity Students
B21
169 oraciones
The Male Reading Crisis
Business EnglishDaily ConversationUniversity Students
Vocabulario y notas de pronunciación para esta lección
Este vídeo tiene 138 frases y 1934 palabras para practicar shadowing. La parte hablada dura 11:01. El hablante habla a un ritmo natural de unas 175 palabras por minuto, cercano a una conversación cotidiana. El 87 % de las palabras está entre las 3.000 más comunes del inglés; conviene repasar el resto antes de empezar.
Vocabulario clave de este vídeo
15 palabras del vídeo que vale la pena aprender, con su pronunciación y significado:
| Palabra | Pronunciación | Significado |
|---|---|---|
| scientist sustantivo | /ˈsaɪəntəst/ | científico, científica |
| correlation sustantivo | /ˌkɔɹ.əˈleɪ.ʃən/ | correlación |
| variable adjetivo | [ˈvæɹ.i.ə.bl̩] | variable |
| relative sustantivo | /ˈɹɛl.ə.tɪv/ | familiar, pariente |
| statistical adjetivo | /stəˈtɪs.tɪ.kəl/ | estadístico |
| pill sustantivo | /pɪl/ | píldora, pastilla |
| uncertainty sustantivo | /ʌnˈsɜːtənti/ | incertidumbre, incerteza |
| sage sustantivo | /seɪd͡ʒ/ | salvia |
| encounter verbo | /ɪnˈkaʊntɚ/ | encontrar, encontrarse |
| significance sustantivo | /sɪɡˈnɪfɪkəns/ | significado, significación |
| inform verbo | /ɪnˈfɔɹm/ | informar, enterar |
| median sustantivo | /ˈmiː.di.ən/ | mediana |
| stat adverbio | /stæt/ | inmediatamente, ya |
| interval sustantivo | /ˈɪntɚvəl/ | intervalo |
| helpful adjetivo | /ˈhɛlp.fl̩/ | servicial, adyuvante |
Phrasal verbs que vas a escuchar
| Palabra | Significado |
|---|---|
| figure out verbo | averiguar, captar |
| go along with verbo | ir junto con |
| show up verbo | comparecer, personarse |
Pronunciación a tener en cuenta
El hablante usa 33 contracciones y formas reducidas, como don't, I'd, they're. Dilas en su forma corta, tal como las oyes.
- Los sonidos de “sh” y “zh”: correlation /ˌkɔɹ.əˈleɪ.ʃən/, partnership /ˈpɑɹtnɚʃɪp/, impression /ɪmˈpɹɛʃn̩/, relation /ɹɪˈleɪʃ(ə)n/, prevention /pɹɪˈvɛnʃən/
- Palabras largas — cuida el acento: correlation /ˌkɔɹ.əˈleɪ.ʃən/, statistical /stəˈtɪs.tɪ.kəl/, uncertainty /ʌnˈsɜːtənti/, significance /sɪɡˈnɪfɪkəns/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/
Cómo practicar con este vídeo
- Escucha el vídeo entero una vez sin hablar y anota las palabras que no conoces.
- Empieza a velocidad 0,75×, haz shadowing frase por frase y vuelve a la velocidad normal cuando te resulte fácil.
- Grábate y compara con el original, prestando atención a palabras como scientist, correlation, variable.
Gramática en este vídeo
Las estructuras que más usa el hablante, con las palabras exactas del vídeo:
| Estructura | En el vídeo |
|---|---|
| Voz pasiva be + participio pasado — importa lo que ocurre, no quién lo hace | are reported · is dragged · be skewed |
| Oraciones de relativo who / which + oración — información extra sobre una persona o cosa | men who died · answer, which is · people who died |
| Present perfect have/has + participio pasado — una acción pasada que sigue importando ahora | has increased · have shown |
¿Qué es la Técnica de Shadowing?
Shadowing es una técnica de aprendizaje de idiomas respaldada por la ciencia, desarrollada originalmente para la formación de intérpretes profesionales y popularizada por el políglota Dr. Alexander Arguelles. El método es simple pero poderoso: escuchas audio en inglés nativo y lo repites en voz alta de inmediato, como una sombra que sigue al hablante con solo 1-2 segundos de retraso. A diferencia de la escucha pasiva o los ejercicios de gramática, el shadowing obliga a tu cerebro y músculos de la boca a procesar y reproducir simultáneamente patrones de habla reales. Las investigaciones muestran que mejora significativamente la precisión de la pronunciación, la entonación, el ritmo, el habla conectada, la comprensión auditiva y la fluidez al hablar, convirtiéndola en una de las metodologías más efectivas para la preparación del IELTS Speaking y la comunicación en inglés en el mundo real.