Hot Coffee

Datasets are automated tests for world models

by Breck Yunits

April 23, 2024 — I wrapped my fingers around the white ceramic mug in the cold air. I felt the warmth on my hands. The caramel colored surface released snakes of steam. I brought the cup to my lips and took a slow sip of the coffee bean flavored water inside.

Happiness is a hot cup of coffee in a ceramic mug on a cold day.

But, like happiness, heat is fleeting.

How can I keep the coffee hot?


I am using a free ceramic mug I got at a work conference.

That's the issue.

I pour my coffee into a ceramic mug I bought at HomeGoods.

Expensive mugs hold heat better.

It gets cold anyway.


My hands can feel the heat rushing from the ceramic. It must be the material.

That's the issue.

I buy an insulated metal mug.

A better material than ceramic is the most important thing for holding heat.

It gets cold anyway.


I combine my theories. I buy an expensive insulated metal mug.

It gets cold anyway.

Wrong again.


Let me try a new approach.

I go back to HomeGoods and buy a thermometer and stopwatch.

I line up the 3 coffee mugs. Pour hot coffee in each.

Every five minutes, I write down the temperature in each mug.

I plot the data in a line chart.

There are some differences, but they are minor.


"What are you doing?" my cousin asks.

I explain the situation.

"Try my mugs, they have covers", he says.

I repeat the experiment and add the new data to my dataset.

The lab. I measured how long these 5 coffee mugs kept coffee hot.


The coffee in my cousin's covered metal mugs stays hot.

Materials and covers are the key.


I still can't explain why.

What is heat?

I know I can build a much better model.

But I have successfully discarded false models.

And now I have a dataset to test all future models against.




I took some narrative liberties above[1], but for accuracy and fun[2], I really did this experiment.

The Raw Data

This is both a dataset on thermal dissipation and penmanship deficiency.

The Data Visualized

Interactive version.

The Typed Data

Index Name Diameter Height Covered 5 10 15 20 25 30 35 40 45 50 55
1 Thermos 3 7.25 TRUE 149.9 147 144.6 141.2 138.2 137.1 134 132.8 131.1 129.3 127.2
2 Yeti Covered 4 3.8 TRUE 146.6 141 136.7 132.5 129 125.6 123.4 121.6 118.5 117.6 115.7
3 Yeti 4 3.8 FALSE 135.5 118.9 107.9 100.5 94.6 90.8 87.6 85.4 82.9 81.3 79.7
4 SF 4 4.5 FALSE 128.8 113.9 102.5 95.9 90.5 86.3 83.6 81.6 79.8 78.2 76.6
5 Aruba 3 3.75 FALSE 130.8 114.6 104.9 98.9 92.3 87.6 85.4 82.9 81.1 80.2 78

The Best Model of Heat

[Coming in a future post]


[1] There was no cousin, nor work conference, nor trips to HomeGoods, I used water and not coffee, and I exaggerated my ignorance and false models. The data and analysis is real.

[2] Everyone[3] agrees making datasets at home is extremely fun.

[3] Dataset needed.

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