ADM-201 dump PMP dumps pdf SSCP exam materials CBAP exam sample questions

总盯着屏幕真的会伤害青少年吗? – 译学馆
未登陆,请登陆后再发表信息
最新评论 (0)
播放视频

总盯着屏幕真的会伤害青少年吗?

Statistics Say Screens Aren't Destroying Today's Teens

[前奏曲]
[♩INTRO]
我们都曾听过这样的新闻报道
So,we’re all used to hearing news stories
内容是关于屏幕和社交媒体如何摧毁我们的生活
about how screens and social media are ruining all of our lives.
来看看我们周围的世界
And,well, looking at the world around us,
确实有这种说法
there’s something to be said for that.
当你看到类似
So when you see a headline like
“观看屏幕时间与青少年抑郁和自杀相关”的标题
“ Screen Time Linked to Depression and Suicide in Teens, ”
这很容易引起非议指责
it’s easy to point fingers and place blame.
智能手机和社交媒体都是有害的
Smartphones and social media are bad.
你应该让孩子尽可能地远离它们 对么?
You should keep your kid away from them as much as possible. Right?
严格来说 并非如此
Well, not exactly.
从大量的心理学研究中
It’s often hard to accurately capture the results
通过快速浏览抢眼标题的方式
from large, psychological studies
很难获得准确的结论
in quick, eye-catching headlines.
同样难以理解的是 为何我们需要研究
And to understand why we need to dive into
一些统计学的错误观点
some misconceptions about statistics.
例如 有个被错误命名的术语
For example, one misleadingly named bit ofjargon
叫做统计显著性
is statistical significance.
当科学家们谈及它的时候
When scientists say that,
他们原意指数据通过一定级别的审查
they mean that their data has passed a certain level of scrutiny,
并且他们由于偶然
and that the odds that the pattern they found
而找到的样品几率很低
was due to chance alone are low.
这些几率通常用p值来表示
Those odds are usually expressed as a p-value,
而p值仅仅是一种比例
which is just the odds as a proportion.
所以p值为0.5意味着有50%的可能性
So a p-value of 0.5 means 50% probability,
或者说是50对50的几率
or 50-50 odds
这等同说明这个数据可能比较有意义
it’s equally likely the data represent something meaningful
因为他们是随机产生的
as it is that they were random luck of the draw.
p值越低 可能性越大
The lower the p-value, the more likely it is
所以你得出结论并非偶然
that it wasn’t chance that you got a result.
而如果被认为“显著”
And for something to be considered “significant”,
科学家经常说它必须
scientists usually say it has to
有一个低于0.05的P值 或者
have a p-value of less than 0.05 or,
要优于1比20的几率
better than 1 in 20 odds. Now,
事物间的联系
the connections between things
比如青少年使用手机或者社会媒体
like teen phone or social media use
和抑郁症状是显著的
and depressive symptoms are significant.
比如 在2017年的一篇研究中
For example, in one 2017 study,
青少年使用社交媒体
the connection between teen social media use
和抑郁症状之间的联系存在一个低于0.001的p值
and depressive symptoms had a p-value of less than 0.001.
这就意味着 它确实不可能是
And that means it’s really unlikely
由于偶然性而得出的结论
that that result was due to chance.
更可能的是
It’s much more likely that there is a link
两个不同的事物之间存在联系
between those two variables.
这就是数据显著性的含义
That’s what statistically significant means.
但是 要注意的是
But there’s a catch,
因为尽管这个测试告诉你存在一种效应
because although this test tells you that an effect exists,
但它并未有告诉你效应有多大
it tells you nothing about how powerful that effect is.
因此 你必须看到这个效应量
And for that, you have to look at the effect size.
在数据中 效应量主要是指
In statistics, effect size refers
一种现象量级的估量
to a measure of the magnitude of a phenomenon basically,
指变量间的联系有多强
how strong the link between the variables is.
在许多心理学研究中 这些“联系”意味着统计中的相关:
In many psychological studies, those “links” mean correlations:
一种数学上两者之间的关联
a mathematical connection between two things.
相关性的效应量大小被称为
And the effect size of a correlation is called
相关系数 它介于0跟1之间
the correlation coefficient, which falls somewhere between zero,
0意味着相互间毫无影响
where there’s really no effect at all,
1意味着两个变量完全同步
and one, where the two variables are perfectlyin sync.
在2007年的研究中
The correlation
社交媒体与抑郁症状的相关系数为0.05
between social media and depressive symptoms in the 2017 study had a coefficient of 0.05.
那真的是没什么关联
That’s really weak.
这意味着我们可以信誓旦旦的说
It means that we can say with confidence that
社交媒体的使用与抑郁症状相关
social medie use is correlated with depressive symptoms.
但是影响是如此之小
But the size of the effect is so tiny that
降低观看屏幕时间并不能真的
reducing screen time won’t really
对青少年的心理健康带来巨大影响
make much of A difference to A teen’s mental health.
所以这就是数据显著性
So it’s statistical significant.
并没有真的产生显著效果
But not really make significant.
这项研究之所以能
The only reason this study was even able
找到如此小而重要的影响
to find such a small, significant effect
唯一原因在于 它有一个庞大的样本量
was that it had a huge sample size
样本包含超过五十万的青少年
of more than five hundred thousand teens.
你懂的 你试图找到的关联效应越是微弱
You see, the weaker the effect you’re trying to find is,
你需要研究的人群就越多
the more people you need to study to see it.
并且似乎数据量越大总是更好
And while it might seem like more data is always better,
大量这样的研究
massive studies like this can kind
才取得了最终的成功
of be a victim of their own success,
当他们能分辨重要但确实微小的影响
as they can identify significant but really tiny effects
这并不真的意味着在实用性层面也是如此
that don’t really mean much on a practical level.
更不必说
Not to mention that it’s really
考虑这种关联为何存在十分重要
important to consider why this correlation exists.
举例来说 如果你被告知社交媒体是有害的
Like,for example, If you’ve been told social media is harmful,
你会很自然的联想花时间盯着屏幕
you might automatically assume the screen time
会妨害青少年心理健康
is causing the teens’ mental health to tank.
但那不是
But that’s not something
研究者搜集的数据能得出的结论
the data the researcherscollected can say.
很容易得出另一种结论
It could very easily be the other way around
青少年感觉越差 他们就越会转向手机
that the worse a teen feels, the more they turn to their phones.
事实上 严格来说 那是一份2019年研究得出的结论
In fact, that’s exactly what a 2019 study
这项研究涵盖了超过12,000名英国学生 它发现
of over 12,000 British students found that
对生活幸福感越低
lower life satisfaction led to
越容易增加社交媒体的使用频率
increased social media use,
尽管研究者们把这种效应量称作“细微”
though the researchers called the size of the effect “trivial”.
并且 我知道我们不停的说
And,I know we say this a lot,
但它仍值得重复:
but it’s worth repeating:
只是因为两者关联并不意味着
just because two things are correlated doesn’t mean
一种会引起另一种变化
that one causes the other.
这种例子很常见 两者同时
It’s often the case that both are influenced
被第三个 也许未知的因素影响
by a third, perhaps unknown factor.
青少年可能偶尔在手机上花更少的时间
Teens might happen to spend less time on their phones
如果他们运动的话 比如
if they do sports, for example,
因为你不能一边浏览Facebook
because you can’t exactly scroll through Facebook
一边踢球
while you’re kicking a soccer ball.
并且运动某种程度上对心理健康产生积极影响
And exercise positively impacts mental health in a way
这与手机或社交媒体毫无关联
that’s unrelated to phones or social media.
同样值得指出的是
It’s also worth pointing out
这些统计显著性的效应可能不是真的
that these statistically significant effects may not be real.
一个0.001的p值听上去非常小
A p-value of 0.001 sounds impressively small,
直到你考虑到 这些发现是
until you consider that these findings are one among many,
一个大型调查里诸多结论中的一个
many others in a very big survey.
调查询问了各种主题 包括锻炼习惯
The survey asked about all sorts of subjects, from exercise habits,
电视节目偏好 宗教服务出席率
to TV viewing, to religious service attendance.
当你要立即寻找大量影响时
And when you’re looking for lots of effects all at once,
很容易得出错误的结论 请记住
it’s much easier to happen across a false positive. Remember,
p值只是几率的度量
a p-value is just a measure of odds
p值为0.001 意味着
a p-value of 0.001 means the odds are 1
1000种情况只会发生1种 它的相关性很低
in 1000 that the correlation is by chance.
没错 那确实小
And yeah, that’s small.
但它意味着你经历了1000或更多的测试
But it means that if you run 1000 or more tests,
你有可能彻头彻尾的错误
you’re downright likely to get a false positive.
为了指出在2017年一份研究中
To point out the flaws with the kind
的分析方面的瑕疵
of analyses run in the 2017 study,
一份2019年研究发表在自然人类行为
a 2019 study published in Nature Human Behavior
它分析了差不多350,000的青少年
analyzed a similarly large dataset of over 350,000 adolescents.
他们发现 诸如戴眼镜 吃土豆泥的事情
And they found that things like wearing glasses and eating potatoes
也会对青少年的幸福产生重要的 轻微的负面影响
also had significant yet small negative effects on the teens’ well-being.
说的更直白些
More to the point,
他们发现关于如何分析数据方面的小决策
they found that small decisions about how to analyze the data like,
类似分析在不同级别的用法之间设立截止点
where to set cut-offs between different levels of use
能把结果
could change the results
从显著的负面影响改变成显著的正面影响!
from a significant negative effect to a significant positive one!
根据大量涉及到成百上千的人的研究
With massive studies where hundreds of thousands
通过他们被调查的很多事情
of people are asked lots of things,
有上亿种方法来验证相关性
there can be trillions of ways to run correlations.
它让那些p值显得不那么令人印象深刻
And that makes those p-values seem a whole lot less impressive.
同样值得注意的是 研究并没有普遍的
It’s also worth noting that studies haven’t universally
把屏幕时间跟社交媒体的使用定性
condemned screen time or social media use.
举例来说 一份2017年的系统审查检查了43份研究
For example, a 2017 systematic review examined 43 studies
这些研究涉及的都是在2003和2013之间的相关主题
on the topic between 2003 and 2013.
然而令人惊讶的是 大多数研究发现
And surprisingly, most of them actually found
社交媒体在青少年幸福感方面有些形形色色的影响或毫无影响
mixed or no effects of social media on adolescent wellbeing.
一些研究者甚至评判
Some researchers even criticized the guidelines put out
世界卫生组织在2019年提出的指导方针
by the World Health Organization in 2019
方针里指出在5岁前
which suggest reducing screen time to
应当把屏幕时间降低到1小时或更短
an hour or less before age five
因为他们所说的证据贫乏到
because they say the evidence so far doesn’ t
无法合理证明岁数上的严格限制
support imposing strict limits at any age.
事实上 所有这些研究都只会
The truth is,all of these studies can only tell you
针对特定的问题告诉你特定的答案
specific answers to specific questions.
比如问题 有多大可能
Questions like, “ how likely is it
自称一周玩X小时电子游戏的人们
that people who self-report playing video games
也会在调查中给出有关负面的回答?
for X hours a week also give depression-related answers on a survey?
那会使得“屏幕时间会导致抑郁”的结论普遍化
That gets generalized to “screen time equals depression”
即使显得过于简单!所以
even though that’s hugely oversimplifying it! So,
青少年的屏幕时间应该被限制吗?
should teen screen time be limited?
也许!我不知道!没人知道!
Maybe! I don’t know! No one knows!
当涉及到类似这样的模糊主题
When it comes to murky subjects like this,
科学能给我们很多信息
science can give us a lot of information.
但有时候 不是所有信息都有用
But sometimes, that information isn’t all that useful.
所以下次你看到
So the next time you see
有新闻说屏幕正在摧毁孩子
a story that says screens are destroying kids
或者你吃的每样东西都会致癌
or everything you eat is going to give you cancer,
花上一分钟来阅读大标题 并判断
take a minute to read beyond the headline and see how much
他们真实谈论的有多大相关性
of an effect they’re really talking about.
一点点的统计学知识能
A little statistical knowledge can go a long way
在让你跟你的孩子在做出更好 更明智的决定方面
towards making better and more informed decisions
大有帮助
for yourself and your kids.
[结尾曲]
[♩OUTRO]

发表评论

译制信息
视频概述

屏幕和社交媒体真的对青少年的身心健康有害么?你需要一些统计学知识来帮助判断。

听录译者

收集自网络

翻译译者

流火

审核员

审核员#LY

视频来源

https://www.youtube.com/watch?v=SYLySBpGGM8

相关推荐