Your Dad, Internet Numbers, and the Categorical Imperative of Asking Smart Questions About Data

# Your Dad, Internet Numbers, and the Categorical Imperative of Asking Smart Questions About Data

You like data. You like snacks. Somewhere your dad is logging trick‑or‑treaters like a bored statistician and the internet is arguing whether 67 quietly toppled 69 in search volume. Someone else made a couple of migration maps. These tiny obsessions are delicious because they’re at once trivial and instructive — perfect teaching examples for anyone who stole a stats textbook in college and never really stopped thinking in numbers.

Think of this as a short field guide: how to seed useful projects, how to visualize what matters, and how to behave when you post the results to a crowd that ranges from kindly beginner to delightfully snarky expert.

## Open threads: where curiosity goes to be useful

If you’re unsure how to start, find the obvious low‑stakes places to ask questions: forum threads, Slack channels, subreddits. These are the labs of intuition. Two etiquette notes that save time and dignity:

– Be precise. “How do I show trend over time?” beats “Visualize my thing.”
– Say what you’re using (Excel, R, Tableau, duct tape) and what you want the reader to take away.

From a logic point of view, you’re simply narrowing the scope of quantification. In predicate logic terms: replace “For some data, visualize it” with “For all timestamps in my CSV, plot f(t).” That kind of constraint changes an amorphous question into one that can be answered.

## Track like your dad — but with intention

There’s dignity in domestic data collection. A long, consistent log beats a flashy one‑off dataset for many purposes. That’s ergodicity whispering in your ear: if you want stable inferences, you need stable processes. Encourage a few extra fields (timestamps, broad age bins, weather notes) and you can move from anecdotes to reproducible claims.

Math tie‑ins:
– Time series analysis and autocorrelation tell you whether your porch rush is a deterministic pattern or a noisy spike. Fourier-ish thinking can spot periodicities (weekday vs. weekend rhythms).
– Cohort comparisons are set theory with a human face: partition your visitors and compare measures across those subsets.

You don’t need a Nobel — you need a reproducible workflow.

## Weird little trends deserve attention (yes, even number memes)

When someone tweets that “67 now tops 69” on Google Trends, your first instinct might be to laugh. Your second should be to ask how the data were normalized. Here’s how discrete anomalies map onto serious math:

– Hypothesis testing: Is the observed bump statistically distinguishable from noise? Beware p‑hacking and multiple comparisons — the more numbers you scan, the more likely something looks significant by accident.
– Bayesian thinking: Put a prior on how plausible such a change is (how often do meme numbers flip?), update with the observed evidence, and see whether your posterior is actually excited.
– Information theory: A spike increases entropy locally, but is the signal meaningful? Look at related queries and geographic distribution to see if the information content is coherent.

Sometimes the right answer is the humbling, “I don’t know.” Data are often better at falsifying simple stories than proving them.

## Maps, migration, and the problem of simple stories

Maps feel powerful because they’re visual stories, but they can lie by omission. Two basic truths:

– Normalize, always. Raw counts mask scale. Likes on a massive platform are not comparable to likes in a small town unless you index by population or relevant denominator.
– Break down cohorts. Year of arrival and age reshape narratives; treating immigrants as a monolith is a category error as bad as conflating apples and oranges.

Tie this to measure theory: you’re choosing a sigma‑algebra of events (which groups to consider) and a measure (counts, shares, rates). Choose poorly and you’ve essentially defined the wrong probability space.

## Where different math disciplines help you ask better questions

A rapid, slightly nerdy tour:

– Probability & Statistics: The obvious home. Understand sampling bias, confidence intervals, and the chorus of false discovery. Simpson’s paradox will sing to you if you don’t stratify by hidden confounders.
– Bayesian inference: When data are scarce or noisy, explicit priors make your assumptions visible instead of letting them sulk in silence.
– Graph theory & networks: Use edge lists and adjacency thinking for social contagion (memes) and migration flows. Visualizing migration as a network often reveals hubs and bridges that maps smooth over.
– Category theory (yes, really): On a philosophical level, categories are about relationships, not raw elements. When you group behaviors (visitors, searches, migrants), think about the morphisms — the processes that map one dataset to another — and be mindful of functoriality: does your transformation preserve the structure you claim?
– Logic (propositional, modal): Spell out your claims. Are you asserting causation or correlation? Modal logic can help formalize counterfactuals: what would have happened without the meme? Pearl’s do‑calculus is the pragmatic cousin here.
– Information theory: Entropy and mutual information tell you whether a visualization actually reduces uncertainty for your audience.

Mixing these perspectives prevents the all‑too‑common sin of latching on to a cute visual and treating it like gospel.

## Presentation, humility, and the internet’s snark

Be crisp. Lead with a single takeaway, then offer two lines of evidence, one paragraph on method, and three caveats. Label axes. Annotate spikes. If someone asks for the raw data, share it or explain constraints. All of this is basic epistemic courtesy.

Also: don’t be coy for attention. Chest‑thumping precision is boring; annotated curiosity is useful. A dash of sarcasm is fine — we’re human — but avoid performative inscrutability.

## The categorical imperative (a small ethical aside)

Kant’s categorical imperative asks: act only on maxims you’d will as universal law. Applied to data: would you design your collection, analysis, and sharing practices if everyone did? If your answer is “no” (I’d hide the source, omit confounders, or mislabel axes), then don’t do it. Treat data practices as universalizable: collect with consent, analyze with transparency, and visualize with clarity.

## Takeaway

Data aren’t magic; they’re stubborn, contextual, and occasionally obsessed with candy. Collect consistently, visualize with purpose, and treat weird findings like invitations to ask better questions, not definitive truths. Whether you’re helping your dad improve his porch metrics, chasing a meme‑driven search spike, or mapping migration flows, be generous with clarity and ruthless with confusion.

So here’s my little provocation for you — less a mic drop, more a ping on Slack: when the next charming anomaly pops up on your feed, what’s the one precise question you’ll ask first, and what would count as convincing evidence?