The most important lesson I have learned throughout this disaster is this: there is no adult stepping into the room to fix things.

YOU are the one who is going to fix things if things are going to be fixed. No one else will.
I keep seeing my colleagues and friends doing amazing things making huge splashes all over. And while they are all wonderful brilliant people, the thing that makes them stand out is this one simple thing:

They didn't stop at "someone should do this." They just did it.
I have learned to no longer rely on the adults, whether they are well-respected people in my field, officials, family, etc. Some just failed to step up, some are getting in the way, and some worse.

I've lost a lot of heroes recently; I'm sure you have too.
I know so many of us have it in our heads that we are little, but I have learned that isn't NEARLY as true as we are told. We're much bigger than they think we are (if we choose to use it) and we're much bigger than WE think we are, if we choose to believe it.
I've had a few successes this year, ones I never would have believed possible. Mostly, I've failed, and failing feels awful. Occasionally I've succeed wildly more than expected, and that feels awful in a different way.

At the start of all of them is a "fuck it, why not."
But pay attention to that "why not" part; there is often a reason. Sometimes the answer is that people aren't doing it because they know something you don't (looking at you armchair epidemiologists).

You've got power, make sure you have the knowledge to wield it well.
Often, the best use of your power is stepping aside and providing a boost to others who are better suited. The most impactful things I've done this year have been helping others do their things. You'll never see that, and that's the point.
We're the adults now whether we like it or not. Use it. Whatever it is, do it.

And get ready, because the next adults are coming up close behind us, and I can't wait to help them step into the room too.

• • •

Missing some Tweet in this thread? You can try to force a refresh
 

Keep Current with Noah Haber

Noah Haber Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @NoahHaber

11 Oct
"Measure twice, cut once" is bullshit.

A brief thread rant on woodworking and causal inference (yeah, you read that right).

From table legs to descriptive stats tables, from picture frames to the framing the big picture for studies. It's gonna get weird, but stick with me.
Let's say you want to make a very simple table. Easy! 4 legs cut to the same length and flat top. Step 1: cut those legs.

So, you take your leg material, and you carefully measure (twice) 26," mark it, and make your cut.

And no matter how careful you were, they don't match.
You might think that you didn't measure carefully enough, or cut straight enough. I promise that's not the problem.

The problem is that you were thinking about the problem the wrong way. Because unless you are a pro, measure twice cut once will NEVER get them to match.
Read 17 tweets
12 Sep
STATS QUIZ!

I have the datapoints below. Nothing hidden, no tricks, just a bunch of data making roughly an ellipse.

In your head, draw what you think the ordinary least squares line (i.e. good ol' y= mx+b) line looks like for these data.
Is this what you drew?
Seems "obvious" right?

Except that's not the OLS line.

The red dashed line is the OLS line.

What's going on here?
Read 13 tweets
5 Jul
Alright fellow epi and econ friends, gather 'round, time for me to talk about this article

Because I am a native econ whose life quest it is to bridge the epi/econ causal inference methods divide.

And because I recognize a lot of my own errors in it.

To outline:

1) The framing is atrocious, in particular since it implies epi-ists < econ-ists

2) The methods divide is very real, but not for the reasons implied.

3) Epi would, indeed, benefit greatly from embracing these methods, but

4) This article only hurts that effort.
The framing here is really bizarre. It starts with saying that RCTs exist and that other approaches can be used, but ONLY mentions the econ-preferred route.

Epi has an entire field of causal inference with observational data. To not even mention it is negligent.
Read 17 tweets
12 Jun
Folks: masks almost certainly help prevent the spread of COVID-19, but the severe flaws in this study render it completely meaningless.

It's like a who's who of basic quantitative fallacies, measurement issues, causal inference failures, and misleading interpretations.
It's hard to write a summary critique of this paper, because it commits ... all of them, plus some creative ones for fun.

You've got ecological fallacy, inappropriate projections, NO treatment of statistical uncertainty (!!!!), attributed causality, non-generalizability, etc.
Already a call for retraction in the works.

I agree; this should never have been published, and should be retracted immediately. And unfortunately, the retraction is going to do more damage to our credibility, but it's the only way forward.
Read 28 tweets
12 Jun
Health researchers tend to default to a "standard" suite of things to control for: age, gender, and race.

Today, I want to talk a bit about what "controlling" for race means, and why it matters. A later thread will talk about interpreting outcomes differences by race.

1/16
Why control for things at all?

The general idea is that we are trying to isolate one thing by "removing" the impact of other things. So, when we "control" for race, we are trying to remove race, so we can focus on something else.

But of course, it isn't that simple.

2/16
What we are "removing" isn't always so obvious, and when we are talking about race, it's EXTREMELY not obvious.

That's because that race variable comes with the infinite well of everything that comes with race and racism.

3/16
Read 19 tweets
4 Jun
Gosh, here it is again.

Now, I have no idea whether these numbers are in any way accurate, but let's take it as true, and explore why it's stupid (and probably means the opposite of what OP thinks).

Causal inference is hard, but ignorance sure is easy.

Thread.
Let's start with a simple one, which is the thing I discussed a few days ago, here:

When you condition on "arrest," you find little differences.

That's important, because the largest portion of the racism part is before/at the point of arrest.
(it is right here where I wish I had used a black square instead of red for shielding OP's identity).

If Black folks are disproportionately arrested, but not disproportionately killed after arrest, that wouldn't show up in the OP's stat.

But it's so much crazier than that!
Read 10 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!

Follow Us on Twitter!