the weeble distribution: a love story

“I’m a statistician,” she wrote. “By day, I work for the census bureau. By night, I use my statistical skills to build the perfect profile. I’ve mastered the mysterious headline, the alluring photo, and the humorous description that comes off as playful but with a hint of an edge. I’m pretty much irresistible at this point.”

“Really?” I wrote back. “That sounds pretty amazing. The stuff about building the perfect profile, I mean. Not the stuff about working at the census bureau. Working at the census bureau sounds decent, I guess, but not amazing. How do you build the perfect profile? What kind of statistical analysis do you do? I have a bit of programming experience, but I don’t know any statistics. Maybe we can meet some time and you can teach me a bit of statistics.”

I am, as you can tell, a smooth operator.

A reply arrived in my inbox a day later:

No, of course I don’t really spend all my time constructing the perfect profile. What are you, some kind of idiot?

And so was born our brief relationship; it was love at first insult.


“This probably isn’t going to work out,” she told me within five minutes of meeting me in person for the first time. We were sitting in the lobby of the Chateau Laurier downtown. Her choice of venue. It’s an excellent place to meet an internet date; if you don’t like the way they look across the lobby, you just back out quietly and then email the other person to say sorry, something unexpected came up.

“That fast?” I asked. “You can already tell you don’t like me? I’ve barely introduced myself.”

“Oh, no, no. It’s not that. So far I like you okay. I’m just going by the numbers here. It probably isn’t going to work out. It rarely does.”

“That’s a reasonable statement,” I said, “but a terrible thing to say on a first date. How do you ever get a second date with anyone, making that kind of conversation?”

“It helps to be smoking hot,” she said. “Did I offend you terribly?”

“Not really, no. But I’m not a very sentimental kind of guy.”

“Well, that’s good.”


Later, in bed, I awoke to a shooting pain in my leg. It felt like I’d been kicked in the shin.

“Did you just kick me in the shin,” I asked.

“Yes.”

“Any particular reason?”

“You were a little bit on my side of the bed. I don’t like that.”

“Oh. Okay. Sorry.”

“I still don’t think this will work,” she said, then rolled over and went back to sleep.


She was right. We dated for several months, but it never really worked. We had terrific fights, and reasonable make-up sex, but our interactions never had very much substance. We related to one another like two people who were pretty sure something better was going to come along any day now, but in the meantime, why not keep what we had going, because it was better than eating dinner alone.

I never really learned what she liked; I did learn that she disliked most things. Mostly our conversations revolved around statistics and food. I’ll give you some examples.


“Beer is the reason for statistics,” she informed me one night while we were sitting at Cicero’s and sharing a lasagna.

“I imagine beer might be the reason for a lot of bad statistics,” I said.

“No, no. Not just bad statistics. All statistics. The discipline of statistics as we know it exists in large part because of beer.”

“Pray, do go on,” I said, knowing it would have been futile to ask her to shut up.

“Well,” she said, “there once was a man named Student…”

I won’t bore you with all the details; the gist of it is that there once was a man by name of William Gosset, who worked for Guinness as a brewer in the early 1900s. Like a lot of other people, Gosset was interested in figuring out how to make Guinness taste better, so he invented a bunch of statistical tests to help him quantify the differences in quality between different batches of beer. Guinness didn’t want Gosset to publish his statistical work under his real name, for fear he might somehow give away their trade secrets, so they made him use the pseudonym “Student”. As a result, modern-day statisticians often work with somethinfg called Student’s t distribution, which is apparently kind of a big deal. And all because of beer.

“That’s a nice story,” I said. “But clearly, if Student—or Gosset or whatever his real name was—hadn’t been working for Guinness, someone else would have invented the same tests shortly afterwards, right? It’s not like he was so brilliant no one else would have ever thought of the same thing. I mean, if Edison hadn’t invented the light bulb, someone else would have. I take it you’re not really saying that without beer, there would be no statistics.”

“No, that is what I’m saying. No beer, no stats. Simple.”

“Yeah, okay. I don’t believe you.”

“Oh no?”

“No. What’s that thing about lies, damned lies, and stat—”

“Statistics?”

“No. Statisticians.”

“No idea,” she said. “Never heard that saying.”

“It’s that they lie. The saying is that statisticians lie. Repeatedly and often. About anything at all. It’s that they have no moral compass.”

“Sounds about right.”


“I don’t get this whole accurate to within 3 percent 19 times out of 20 business,” I whispered into her ear late one night after we’d had sex all over her apartment. “I mean, either you’re accurate or you’re not, right? If you’re accurate, you’re accurate. And if you’re not accurate, I guess maybe then you could be within 3 percent or 7 percent or whatever. But what the hell does it mean to be accurate X times out of Y? And how would you even know how many times you’re accurate? And why is it always 19 out of 20?”

She turned on the lamp on the nightstand and rolled over to face me. Her hair covered half of her face; the other half was staring at me with those pale blue eyes that always looked like they wanted to either jump you or murder you, and you never knew which.

“You really want me to explain confidence intervals to you at 11:30 pm on a Thursday night?”

“Absolutely.”

“How much time do you have?”

“All, Night, Long,” I said, channeling Lionel Richie.

“Wonderful. Let me put my spectacles on.”

She fumbled around on the nightstand looking for them.

“What do you need your glasses for,” I asked. “We’re just talking.”

“Well, I need to be able to see you clearly. I use the amount of confusion on your face to gauge how much I need to dumb down my explanations.”


Frankly, most of the time she was as cold as ice. The only time she really came alive—other than in the bedroom—was when she talked about statistics. Then she was a different person: excited and exciting, full of energy. She looked like a giant Tesla coil, mid-discharge.

“Why do you like statistics so much,” I asked her over a bento box at ZuNama one day.

“Because,” she said, “without statistics, you don’t really know anything.”

“I thought you said statistics was all about uncertainty.”

“Right. Without statistics, you don’t know anything… and with statistics, you still don’t know anything. But with statistics, we can at least get a sense of how much we know or don’t know.”

“Sounds very… Rumsfeldian,” I said. “Known knowns… unknown unknowns… is that right?”

“It’s kind of right,” she said. “But the error bars are pretty huge.”

“I’m going to pretend I know what that means. If I admit I have no idea, you’ll think I wasn’t listening to you in bed the other night.”

“No,” she said. “I know you were listening. You were listening very well. It’s just that you were understanding very poorly.”


Uncertainty was a big theme for her. Once, to make a point, she asked me how many nostrils a person breathes through at any given time. And then, after I experimented on myself and discovered that the answer was one and not two, she pushed me on it:

“Well, how do you know you’re not the only freak in the world who breathes through one nostril?”

“Easily demonstrated,” I said, and stuck my hand right in front of her face, practically covering her nose.

“Breathe out!”

She did.

“And now breathe in! And then repeat several times!”

She did.

“You see,” I said, retracting my hand once I was satisfied. “It’s not just me. You also breathe through one nostril at a time. Right now it’s your left.”

“That proves nothing,” she said. “We’re not independent observations; I live with you. You probably just gave me your terrible mononarial disease. All you’ve shown is that we’re both sick.”

I realized then that I wasn’t going to win this round—or any other round.

“Try the unagi,” I said, waving at the sushi in a heroic effort to change the topic.

“You know I don’t like to try new things. It’s bad enough I’m eating sushi.”

“Try the unagi,” I suggested again.

So she did.

“It’s not bad,” she said after chewing on it very carefully for a very long time. “But it could use some ketchup.”

“Don’t you dare ask them for ketchup,” I said. “I will get up and leave if you ask them for ketchup.”

She waved her hand at the server.


“There once was a gentleman named Bayes,” she said over coffee at Starbucks one morning. I was running late for work, but so what? Who’s going to pass up the chance to hear about a gentleman named Bayes when the alternative is spending the morning refactoring enterprise code and filing progress reports?

“Oh yes, I’ve heard about him,” I said. “He’s the guy who came up with Bayes’ theorem.” I’d heard of Bayes theorem in some distant class somewhere, and knew it had something to do with statistics, though I had not one clue what it actually referred to.

“No, the Bayes I’m talking about is John Bayes—my mechanic. He’s working on my car right now.”

“Really?”

“No, not really, you idiot. Yes, Bayes as in Bayes’ theorem.”

“Thought so. Well, go ahead and tell me all about him. What is John Bayes famous for?”

“Bayes’ theorem.”

“Huh. How about that.”

She launched into a very dry explanation of conditional probabilities and prior distributions and a bunch of other terms I’d never heard of before and haven’t remembered since. I stopped her about three minutes in.

“You know none of this helps me, right? I mean, really, I’m going to forget anything you tell me. You know what might help, is maybe if instead of giving me these long, dry explanations, you could put things in a way I can remember. Like, if you, I don’t know, made up a limerick. I bet I could remember your explanations that way.”

“Oh, a limerick. You want a Bayesian limerick. Okay.”

She scrunched up her forehead like she was thinking very deeply. Held the pose for a few seconds.

“There once was a man named John Bayes,” she began, and then stopped.

“Yes,” I said. “Go on.”

“Who spent most of his days… calculating the posterior probability of go fuck yourself.”

“Very memorable,” I said, waving for the check.


“Suppose I wanted to estimate how much I love you,” I said over asparagus and leek salad at home one night. “How would I do that?”

“You love me?” she arched an eyebrow.

“Good lord no,” I laughed hysterically. “It’s a completely and utterly hypothetical question. But answer it anyway. How would I do it?”

She shrugged.

“That’s a measurement problem. I’m a statistician, not a psychometrician. I develop and test statistical models. I don’t build psychological instruments. I haven’t the faintest idea how you’d measure love. As I’m sure you’ve observed, it’s something I don’t know or care very much about.”

I nodded. I had observed that.

“You act like there’s a difference between all these things there’s really no difference between,” I said. “Models, measures… what the hell do I care? I asked a simple question, and I want a simple answer.”

“Well, my friend, in that case, the answer is that you must look deep into your own heart and say, heart, how much do I love this woman, and then your heart will surely whisper the answer delicately into your oversized ear.”

“That’s the dumbest thing I’ve ever heard,” I said, tugging self-consciously at my left earlobe. It wasn’t that big.

“Right?” she said. “You said you wanted a simple answer. I gave you a simple answer. It also happens to be a very dumb answer. Well, great, now you know one of the fundamental principles of statistical analysis.”

“That simple answers tend to be bad answers?”

“No,” she said. “That when you’re asking a statistician for help, you need to operationalize your question very carefully, or the statistician is going to give you a sensible answer to a completely different question than the one you actually care about.”


“How come you never ask me about my work,” I asked her one night as we were eating dinner at Chez Margarite. She was devouring lemon-infused pork chops; I was eating a green papaya salad with mint chutney and mango salsa dressing.

“Because I don’t really care about your work,” she said.

“Oh. That’s… kind of blunt.”

“Sorry. I figured I should be honest. That’s what you say you want in a relationship, right? Honesty?”

“Sure,” I said, as the server refilled our water glasses.

“Well,” I offered. “Maybe not that much honesty.”

“Would you like me to feign interest?”

“Maybe just for a bit. That might be nice.”

“Okay,” she sighed, giving me the green light with a hand wave. “Tell me about your work.”

It was a new experience for me; I didn’t want to waste the opportunity, so I tried to choose my words carefully.

“Well, for the last month or so, I’ve been working on re-architecting our site’s database back-end. We’ve never had to worry about scaling before. Our DB can handle a few dozen queries per second, even with some pretty complicated joins. But then someone posts a product page to reddit because of a funny typo, and suddenly we’re getting hundreds of requests a second, and all hell breaks loose.”

I went on to tell her about normal forms and multivalued dependencies and different ways of modeling inheritance in databases. She listened along, nodding intermittently and at roughly appropriate intervals. But I could tell her heart wasn’t in it. She kept looking over with curiosity at the group of middle-aged Japanese businessmen seated at the next table over from us. Or out the window at the homeless man trying to sell rhododendrons to passers-by. Really, she looked everywhere but at me. Finally, I gave up.

“Look,” I said, “I know you’re not into this. I guess I don’t really need to tell you about what I do. Do you want to tell me more about the Weeble distribution?”

Her face lit up with excitement; for a moment, she looked like the moon. A cold, heartless, beautiful moon, full of numbers and error bars and mascara.

Weibull,” she said.

“Fine,” I said. “You tell me about the Weibull distribution, and I’ll feign interest. Then we’ll have crème brulee for dessert, and then I’ll buy you a rhododendron from that guy out there on the way out.”

“Rhododendrons,” she snorted. “What a ridiculous choice of flower.”


“How long do you think this relationship is going to last,” I asked her one brisk evening as we stood outside Gordon’s Gourmets with oversized hot dogs in hand.

I was fully aware our relationship was a transient thing—like two people hanging out on a ferry for a couple of hours, both perfectly willing to having a reasonably good time together until the boat hits the far side of the lake, but neither having any real interest in trading numbers or full names.

I was in it for—let’s be honest—the sex and the conversation. As for her, I’m not really sure what she got out of it; I’m not very good at either of those things. I suppose she probably had a hard time finding anyone willing to tolerate her for more than a couple of days.

“About another month,” she said. “We should take a trip to Europe and break up there. That way it won’t be messy when we come back. You book your plane ticket, I’ll book mine. We’ll go together, but come back separately. I’ve always wanted to end a relationship that way—in a planned fashion where there are no weird expectations and no hurt feelings.”

“You think planning to break up in Europe a month from now is a good way to avoid hurt feelings?”

“Correct.”

“Okay, I guess I can see that.”


And that’s pretty much how it went. About a month later, we were sitting in a graveyard in a small village in southern France, winding our relationship down. Wine was involved, and had been involved for most of the day; we were both quite drunk.

We’d gone to see this documentary film about homeless magicians who made their living doing card tricks for tourists on the beaches of the French Riviera, and then we stumbled around town until we came across the graveyard, and then, having had a lot of wine, we decided, why not sit on the graves and talk. And so we sat on graves and talked for a while until we finally ran out of steam and affection for each other.

“How do you want to end it,” I asked her when we were completely out of meaningful words, which took less time than you might imagine.

“You sound so sinister,” she said. “Like we’re talking about a suicide pact. When really we’re just two people sitting on graves in a quiet cemetery in France, about to break up forever.”

“Yeah, that. How do you want to end it.”

“Well, I like endings like in Sex, Lies and Videotape, you know? Endings that don’t really mean anything.”

“You like endings that don’t mean anything.”

“They don’t have to literally mean nothing. I just mean they don’t have to have any deep meaning. I don’t like movies that end on some fake bullshit dramatic note just to further the plot line or provide a sense of closure. I like the ending of Sex, Lies, and Videotape because it doesn’t follow from anything; it just happens.”

“Remind me how it ends?”

“They’re sitting on the steps outside, and Ann—-Andie McDowell’s character–says “I think it’s going to rain. Then Graham says, “it is raining.” And that’s it. Fade to black.”

“So that’s what you like.”

“Yes.”

“And you want to end our relationship like that.”

“Yes.”

“Okay,” I said. “I guess I can do that.”

I looked around. It was almost dark, and the bottle of wine was empty. Well, why not.

I think it’s going to rain,” I said.

Jesus,” she said incredulously, leaning back against a headstone belonging to some guy named Jean-Francois. ” I meant we should end it like that. That kind of thing. Not that actual thing. What are you, some kind of moron?”

“Oh. Okay. And yes.”

I thought about it for a while.

“I think I got this,” I finally said.

“Ok, go,” she smiled. One of the last—and only—times I saw her smile. It was devastating.

“Okay. I’m going to say: I have some unfinished business to attend to at home. I should really get back to my life. And then you should say something equally tangential and vacuous. Something like: ‘yes, you really should get back there. Your life must be lonely without you.'”

“Your life must be lonely without you…” she tried the words out.

“That’s perfect,” she smiled. “That’s exactly what I wanted.”


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