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Office Hours

The struggles of science at a fast-moving startup

Even hired its data team a few months after launch. Our mandate was simple: figure out if we were doing the right thing. Everyone at the company wanted to know if the product was optimizing for impact, and to optimize we needed numbers: risks to minimize and objectives to maximize.

“This is the first time we’ve ever done something like this.” I remember Jon being openly uncertain at my final onsite. The company still didn’t know about this whole ‘data science’ thing. It was a risk, both to Even and to me. …

Welcome to the birth of a new digital marketplace

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Wind the clock back three decades. Pick a random kid off the street and ask them what they want to be when they grow up.

I’d bet a hundred dollars on them saying “Astronaut”.

It would just make sense. The shuttle missions were still in full swing and Tom Hanks had charmed our pants off in Apollo 13. By 2000, we had a permanent fucking space station in low-Earth orbit, with astronauts and cosmonauts breaking bread in microgravity.

It was hard not to fall in love with the idea of life in zero-G.

Today, if you did that same exercise…

People who watched the video will get the joke
People who watched the video will get the joke

Election fraud in Michigan? Nope, just a huckster

Dr. Shiva Ayyadurai is doubling down on a video which I poked holes in a few days ago. In it, he claimed that Joe Biden stole more than 60,000 votes in Michigan. It involved poor mathematics, and folks like Matt Parker of StandupMaths drew similar conclusions.

Stanford Ph.D and director of MIT’s Election Lab, Charles Stewart III, happens to agree with us.

In this new video, Ayyadurai dismisses math-based criticisms by saying that detecting election-fraud is “not a math problem, but a pattern-recognition problem.”

Luckily, pattern recognition is my main discipline and the basis for my professional career —…

Election Fraud in Michigan? Nope, just being misleading with data.

A few days ago, Dr. Shiva Ayyadurai posted a video that claimed to prove election fraud in Michigan. He is wrong, and I’ll show you how using data from Oakland County, Michigan. My code and data sources are public and replicable — and everything I write is open for comment and discussion.

Previously, I posted a detailed takedown of how his analysis was a mathematical parlor trick — which he uses to generate a “suspicious” result that’s supposed to prove that Biden stole 60,000+ votes from Trump.

Election Fraud in Michigan? Nope: just how lines work

NOTE: On Nov. 16th, Ayyadurai doubled down on his misleading analyses.

On November 10th, Dr. Shiva Ayyadurai posted a video claiming that some simple analytics revealed election fraud in Michigan. It received more than 200,000 views, and claims that Joe Biden stole more than 60,000 votes in Michigan.

The main thrust of his analysis is a mathematical parlor trick. In a separate post, I play that parlor trick myself with Oakland County data to “prove” the opposite conclusion — showing that his analysis is bogus at its core.

Feel free to watch it if you like — see if you…

Future, Mathematics, Opinion

Definition and implications of a new kind of epistemology

Source: Todd Quackenbush at Unsplash

We have officially entered the post-truth era.

With the rise of deep-fakes, lying politicians, and Surkovian disinformation campaigns, it’s hard to get a handle on what truth even is.

For a few months I was deep in a skeptical hole where I had truly lost grip on what I considered “real”, and I had to claw my way out by getting real silly and coming up with a formal definition that we might all agree with. Truth, I propose, is given by this expression:

Big Population + Big Data = Critical Failure

Photo by Joshua Coleman on Unsplash

We’ve been sold a false promise.

Somewhere down the line we tricked ourselves into thinking that truth was a side-effect of volume. “If we collect enough data,” we said, “our overwhelming statistical power will blow a hole in the unknown.”

Instead, we shot ourselves in the foot.

In his article Statistical Paradises & Paradoxes In Big Data, the Harvard statistician (and certifiable genius, as far as I’m concerned) Xiao-li Meng sets down a rigorous proof of just how bad we screw ourselves when we collect data without regard for exactly how it’s collected.

He draws upon mathematics that are elegant…

Randomized controlled trials, imperfect compliance, and the counterfactual time machine

what if I told you that by the end of this post you’ll understand what this picture means
what if I told you that by the end of this post you’ll understand what this picture means

We build software to solve human problems. But human problems can be messy, and sometimes it’s not terribly clear whether or not we’ve actually solved them.

Snapchat might tell they’re successful if they see 50% of regular users check out their new dog filter, and Facebook could say they’ve shattered their growth milestones by showing they’ve achieved more than 2.3 billion monthly active users.

But what’s your acceptance criteria when your app is designed to help members cope with anxiety? What metric can you monitor when your software was built to cultivate mindfulness?

Or, in the case of my company—Even

Naim Kabir

Data scientist at — focused on experimentation, causal inference, causal discovery, & explainable machine learning.

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