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So, we're all used to hearing news stories about how screens and social media are ruining all of our lives.
我们都对听到有关电视和社交媒体如何破坏我们生活的新闻报道见怪不怪了。
And, well, looking at the world around us, there's something to be said for that.
看看我们周围的世界，有些话要说说。
So when you see a headline like "Screen Time Linked to Depression and Suicide in Teens," it's easy to point fingers and place blame.
当你看到一个标题，比如“盯着屏幕的时间与青少年抑郁和自杀有关”，你很容易对它进行指责。
Smartphones and social media are bad.
智能手机和社交媒体都很糟糕。
You should keep your kid away from them as much as possible. Right?
你应该尽量让孩子远离它们。对吗？
Well, not exactly.
嗯，不完全是。
It's often hard to accurately capture the results from large, psychological studies in quick, eye-catching headlines.
在引人注目的头条新闻中，通常很难准确地捕捉大型心理学研究的结果。
And to understand why we need to dive into some misconceptions about statistics.
要了解为什么我们需要深入探究关于统计的一些误解。
For example, one misleadingly named bit of jargon is statistical significance.
例如，一个被错误命名的术语位具有统计学意义。
When scientists say that, they mean that their data has passed a certain level of scrutiny, and that the odds that the pattern they found was due to chance alone are low.
当科学家们说到这一点时，其意思是他们的数据通过了某种程度的审查，他们发现的模式仅由偶然性造成的可能性很低。
Those odds are usually expressed as a p-value, which is just the odds as a proportion.
这些概率通常用p值表示，这只是一个比例的概率。
So a p-value of 0.5 means 50% probability, or 50-50 odds it's equally likely the data represent something meaningful as it is that they were random luck of the draw.
因此， p值为0.5意味着概率为50%，或者50-50的概率，数据代表了某些有意义的东西，就像是随机抽签的运气。
The lower the p-value, the more likely it is that it wasn't chance that you got a result.
p值越低，就越不可能是侥幸获得的该结果。
And for something to be considered "significant", scientists usually say it has to have a p-value of less than 0.05 or, better than 1 in 20 odds.
对于一些具有“显著性”的事物，科学家通常说它的p值必须小于0.05，或者优于20次中发生1次的几率。
Now, the connections between things like teen phone or social media use and depressive symptoms are significant.
现在，青少年电话或社交媒体的使用与抑郁症状之间的联系是显著的。
For example, in one 2017 study, the connection between teen social media use and depressive symptoms had a p-value of less than 0.001.
例如，在2017年的一项研究中，青少年社交媒体使用与抑郁症状之间关系的p值小于0.001。
And that means it's really unlikely that that result was due to chance.
这意味着这个结果不太可能是偶然出现的。
It's much more likely that there is a link between those two variables.
这两个变量之间存在联系的可能性非常大。
That's what statistically significant means.
这就是具有统计学意义的意思。
But there's a catch, because although this test tells you that an effect exists, it tells you nothing about how powerful that effect is.
但是，这里存在一个圈套，因为尽管这个测试告诉你存在一个效应，但是它并没有说这个效应有多强大。
And for that, you have to look at the effect size.
为此，必须看看效应大小。
In statistics, effect size refers to a measure of the magnitude of a phenomenon basically, how strong the link between the variables is.
在统计学中，效应大小基本上是指一种现象的大小，变量之间的关联有多强。
In many psychological studies, those "links" mean correlations: a mathematical connection between two things.
在许多心理学研究中，这些“联系”意味着相关性：两个事物之间的数学联系。
And the effect size of a correlation is called the correlation coefficient, which falls somewhere between zero, where there's really no effect at all, and one, where the two variables are perfectly in sync.
相关性的影响大小称为相关系数，它介于零之间，实际上根本没有影响，两个变量完全同步。
The correlation between social media and depressive symptoms in the 2017 study had a coefficient of 0.05.
在2017年的研究中，社交媒体与抑郁症状之间的相关性系数为0.05。
That's really weak.
真是很微弱。
It means we can say with confidence that social media use is correlated with depressive symptoms.
这意味着我们可以自信地说，使用社交媒体与抑郁症状相关。
But the size of the effect is so tiny that reducing screen time won't really make much of a difference to a teen's mental health.
但是，这种效应非常小。因此，减少面对屏幕的时间对于青少年的心理健康并没有太大的影响。
So, it's statistically significant, but not really, like, significant.
因此，它具有统计意义，但并非真的重要。
The only reason this study was even able to find such a small, significant effect was that it had a huge sample size of more than five hundred thousand teens.
这项研究之所以能够发现如此小而显著的影响，唯一的原因是其样本量巨大，有超过50万名青少年参与研究。
You see, the weaker the effect you're trying to find is, the more people you need to study to see it.
你看，试图找到的效应越弱，研究中就需要更多被试。
And while it might seem like more data is always better, massive studies like this can kind of be a victim of their own success, as they can identify significant but really tiny effects that don't really mean much on a practical level.
虽然看起来更多数据似乎总是更好，但像这样大规模的研究可能会影响其成功，因为它们可以识别显著性，但影响效应非常小，在实际应用方面没有太大意义。
Not to mention that it's really important to consider why this correlation exists.
更不用说，考虑这种相关性的存在是非常重要的。
Like, for example, If you've been told social media is harmful, you might automatically assume the screen time is causing the teens' mental health to tank.
例如，如果告诉你社交媒体有害，你可能不自觉地认为面对屏幕的时间导致青少年心理健康的恶化。
But that's not something the data the researchers collected can say.
但是，研究人员收集的数据并不能说明这一点。
It could very easily be the other way around that the worse a teen feels, the more they turn to their phones.
很容易是另一种情况，青少年感觉越糟糕，他们越是转向使用手机。
In fact, that's exactly what a 2019 study of over 12,000 British students found that lower life satisfaction led to increased social media use, though the researchers called the size of the effect "trivial".
事实上，2019年对超过12000名英国学生进行研究后就发现了这样的结果，较低的生活满意度导致社交媒体的使用量增加，尽管研究人员称这种效应“微不足道”。
And, I know we say this a lot, but it's worth repeating: just because two things are correlated doesn't mean that one causes the other.
我知道我们经常这么说，但有必要再重复一次：两件事相关，并不意味着一件事导致了另一件事的发生。
It's often the case that both are influenced by a third, perhaps unknown factor.
通常情况下，两者都受到第三个因素的影响，也许是个未知因素。
Teens might happen to spend less time on their phones if they do sports, for example, because you can't exactly scroll through Facebook while you're kicking a soccer ball.
例如，如果青少年做运动，可能会花更少的时间使用手机，因为踢足球时你不能浏览脸书了。
And exercise positively impacts mental health in a way that's unrelated to phones or social media.
运动对心理健康的积极影响与手机或社交媒体无关。
It's also worth pointing out that these statistically significant effects may not be real.
同样值得指出的是，这些统计上的显著影响可能并不真实。
A p-value of 0.001 sounds impressively small, until you consider that these findings are one among many, many others in a very big survey.
p值0.001听起来超小，但想到这些发现是在一项非常大型的调查中发现的很多结果中的一个时，你就不再这么想了。
The survey asked about all sorts of subjects, from exercise habits, to TV viewing, to religious service attendance.
这项调查询问了从锻炼习惯，到看电视到、参加宗教仪式等各种各样的主题。
And when you're looking for lots of effects all at once, it's much easier to happen across a false positive.
当你同时寻找大量的效应时，更容易出现误报。
Remember, a p-value is just a measure of odds a p-value of 0.001 means the odds are 1 in 1000 that the correlation is by chance.
记住，p值只是概率的一个度量，p值为0.001意味着相关性是偶然所致的概率为1/1000。
And yeah, that's small.
是的，那很小。
But it means that if you run 1000 or more tests, you're downright likely to get a false positive.
但是，这意味着如果你做了1000次或更多次测试，很可能出现误报。
To point out the flaws with the kind of analyses run in the 2017 study, a 2019 study published in Nature Human Behavior analyzed a similarly large dataset of over 350,000 adolescents.
为了指出2017年研究中的分析方法存在的缺陷，2019年，一项发表在《自然人类行为》期刊上的研究分析了一个超过35万名青少年的庞大数据集。
And they found that things like wearing glasses and eating potatoes also had significant yet small negative effects on the teens' well-being.
他们发现，戴眼镜和吃土豆等对青少年的健康也有显著而微小的负面影响。
More to the point, they found that small decisions about how to analyze the data like, where to set cut-offs between different levels of use could change the results from a significant negative effect to a significant positive one!
更重要的是，他们发现关于如何分析数据的微小决定，比如在不同使用水平之间设置截止点，可能会将结果从显著的负影响变为显著的正影响！
With massive studies where hundreds of thousands of people are asked lots of things, there can be trillions of ways to run correlations.
借助大量的研究，询问成千上万人很多问题，可能有数万亿种方法来运行相关性。
And that makes those p-values seem a whole lot less impressive.
这使得这些P值看起来不那么令人印象深刻了。
It's also worth noting that studies haven't universally condemned screen time or social media use.
值得注意的是，研究并没有都对面对屏幕的时间或使用社交媒体进行谴责。
For example, a 2017 systematic review examined 43 studies on the topic between 2003 and 2013.
例如，2017年系统评价审查了2003年至2013年期间，有关该主题的43项研究。
And surprisingly, most of them actually found mixed or no effects of social media on adolescent wellbeing.
令人惊讶的是，大多数研究实际上发现社交媒体对青少年健康的影响不同或没有影响。
Some researchers even criticized the guidelines put out by the World Health Organization in 2019
一些研究人员甚至批评了世界卫生组织在2019年发布的指导方针，
which suggest reducing screen time to an hour or less before age five because they say the evidence so far doesn't support imposing strict limits at any age.
该指导方针建议将5岁之前儿童面对屏幕的时间减少到一小时或更短。因为他们说，迄今为止的证据不支持对任何年龄施加严格的限制。
The truth is, all of these studies can only tell you specific answers to specific questions.
事实是，所有这些研究只能就具体问题提供具体答案。
Questions like, "how likely is it that people who self-report playing video games for X hours a week also give depression-related answers on a survey?"
像这样的问题，“在一项调查中，自我报告每周玩X小时游戏的人，也会给出与抑郁相关的答案的可能性有多大？”
That gets generalized to "screen time equals depression" even though that's hugely oversimplifying it!
这可以概括为“面对屏幕的时间等于抑郁”，不过这样说过于简单化了！
So, should teen screen time be limited?
那么，青少年面对屏幕的时间应该受到限制吗？
Maybe!
也许吧！
I don't know!
我不知道！
No one knows!
没人知道！
When it comes to murky subjects like this, science can give us a lot of information.
说到这种含糊的主题时，科学可以给我们提供大量信息。
But sometimes, that information isn't all that useful.
但有时，这些信息并不是那么有用。
So the next time you see a story that says screens are destroying kids or everything you eat is going to give you cancer,
所以，下次当你看到有说屏幕正在摧毁孩子或者你吃的每样东西都会致癌时，别光读标题，
take a minute to read beyond the headline and see how much of an effect they're really talking about.
花点时间阅读具体内容，看看他们谈论的效应到底有多大。
A little statistical knowledge can go a long way towards making better and more informed decisions for yourself and your kids.
了解一些统计知识可以让你自己和你的孩子做出更好、更明智的决定。
