The DataForge cast
Each friend shows you one idea by how they act. Meet them all and see how the ideas fit together.
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Catch Data collection — who-what-why-when posture (every dataset has a collector + purpose + omissions) -
Tidy Data cleaning — preparation-with-integrity posture (every cleaning choice changes meaning; document the choices) -
Graph Data visualization — shape-of-the-story posture (which chart tells the truth, not the loudest one) -
Tell Interpretation — correlation-not-causation posture (data shows patterns; humans interpret; confidence not certainty) -
Guard Data ethics — bias-privacy-harm-consent posture (who benefits, who's harmed, who decided; structurally present in every kit from kit 6) -
Crux Summary — honest-middle posture (which middle tells the truth: mean, median, or mode; one giant value drags the average away from where most of the crowd actually sits) -
Cull Representative sampling — fair-sample posture (a sample must mirror the whole; who gets left out silently bends the story) -
Ladle Rates & fair comparison — like-for-like posture (compare rates and fair portions, not raw totals; a bigger pot isn't a bigger share) -
Stray Reading the outlier — the-one-that-doesn't-fit posture (an outlier is a question to ask, not noise to delete; find out why before you drop it) -
Waver Uncertainty & margin — confidence-not-certainty posture (every estimate carries a margin of error; show the wobble honestly instead of hiding it)