90% of the T Distribution | Hacker News Reader

90% of the T Distribution

entropicthoughts.com

60 pointsibobev19 comments

Author here. It feels a bit pushy and uncharacteristic for me, but now that this ended up on the front page I would really appreciate if I could get 3.5 minutes out of your life, by taking this keyboard latency probe: https://xkqr.org/bellwether/keyboardtest.html

I've already received very good data, but there are some tenuous connections that are still too noisy to be certain of

- Are Apple keyboards really slower that non-Apple keyboards?

- Are cheap keyboards really faster than expensive keyboards?

- Are virtually all split keyboards programmable?

More samples would help nail these things down. I'll share the analysis with the community once I'm done, of course. (The analysis pipeline is mostly automated so I can work on analysis in parallel with receiving more submissions.)

How do you distinguish between input (Keyboard/USB) and output (Browser/Graphics stack) delays in this test?

That's outside the scope of what I wanted to find out so my plan was to treat that as part of the random effects varying from user to user.

But now that you say it, I could annotate the data with a guess about OS and browser from the user agent string. Cool idea. Thanks!

It won't be very random though. E.g. Apple keyboards will almost always be on device with Apple's hardware & stack, cheap keyboards will tend to be with cheap computers/monitors, expensive keyboards the opposite.

It'll still be interesting data (and I'll dump as many of my systems in as I can), but it will take a lot more than treating differences as noise to answer those kinds of questions in a meaningful way.

That is indeed an annoyance. Last time I looked at the data the "Apple keyboard" and "Apple computer" submissions overlapped so much I couldn't include them separately in the model.

Similarly, I can't separate the effects of programmable and split keyboards because those two almost perfectly overlap in the data.

I hope getting more submissions will help at least a little bit with this.

It might help to pose the problem as regression analysis with with categorical dependent variables. Perhaps with generalized linear model to account for the case that the predicted variable is positive.

Alternatively, if you have enough data, see if an orthogonal array design is feasible. It will not be very kosher because you would be selecting as opposed to assigning.

My current best attempt is a mixed-effects quantile regression (to capture the influence of the fixed effects on the tenth percentile while accounting for dependence between trials from the same person) but it is so compute hungry with this (apparently relatively large) data set that I'm looking into Bayesian methods for accomplishing similar things.

Quantile regression is such an under-used tool. It really needs to be better known.

Unless I'm mistaken, this uses "standard deviation" to refer to standard error throughout. They differ by a factor of sqrt(num_samples).

This is actually much more commonly useful than the t distribution, in my experience. You can squint at a histogram (or some summary stats), eyeball the stdev, approximate the stderr in your head, and get a pretty good sense of confidence.

I most often find myself doing this for the Bernoulli distribution, where it's also handy to know that the stdev is sqrt(p(1-p)), or "about 1/2 if p is middling, or sqrt(p) when it's small" (and you can flip the polarity to handle p→1).

Not to be too picky, but I think when the author refers to ‘number of samples’, he means ‘sample size’. One sample, one or more observations.

This is neither here nor there: I was reading the about page of the author, and it contains a passage that slightly confused me: "My name is Chris and I live in Sweden. I have a beautiful, supportive wife whose love I will never be able to requite, neither in degree nor kind." English isn't my first language, how should the second sentence be interpreted?

My interpretation would be that he feels his wife is incredibly loving in a quantity he isn’t able to match (degree) and in a unique way he’s not able to match (kind). General life experience plus the fact that he wrote that tells me he’s probably wrong and his wife would probably say the same about him, but that’s just speculation.

That it's not possible for him to love her back as much as she does. "Requite" is quite an obscure word, I've only ever seen it used in the phrase "unrequited love", which means a love which isn't returned (in quite a different sense than what is used here, since I assume that the author didn't mean that he didn't love his wife, only that his love didn't measure up).

A kind of literal translation would be that he cannot match the amount of love and support that his wife shows, both in amount or in intensity.

A rewording might be "she is more supportive than I could ever be, and better at being supportive than I could ever be"

Poetic way of saying that he is really thankful for her and is indebted to her (not in a literal monetary sense, just that her support and love is without bound, such that his own can never measure up against it).

I have a wife. I love my wife. My wife loves me. I cannot return my wife’s love for me at the same amount or manner. She loves me more than I can ever love her. She loves me in ways I can never.

It’s very poetically written and sounds very loving. My simple translation loses a lot of beauty.

To address the degree bit, one could bake a couple of pies once in a while.

Let me show myself out.

This is good on many levels. It hits temperature degrees, angular degrees, and it's actually accurate advice.

Was entirely tongue in cheek. A long time admirer of your blog.

Having a wonderful wife myself I can understand the feelings.

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