Share a time when you had to translate a highly complex mathematical or data science concept to a non-technical executive to secure project buy-in.

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Anonymous · Accepted Answer

When we report an exp result we report 3 things: * The point estimate of the experient result * Is it statistically sig * P-value * Ci The first 2, folks typically don’t have an issue with understanding, on the p-value I like to remind them that this is not the p(there is no true effect), it’s a measure of how surprising the result we observed (or a more extreme result) is assuming we take the pessimistic (assuming we expect improvement) view that our change had no effect, and the p-value is a probability, the smaller the probability the more surprising the result, so being pessimistic, we only reject our view that there is no effect if the p-value is below some threshold. I typically like to explain this to stakeholders during the design stage of the experiment itself and explain what an alpha of 5/10% means. So if p-value 4% means, if there was no improvement from our launch then there would be a 4 % chance the observed result is due to chance alone. It does NOT mean there is a 96% chance the effect is real The CI is the most interesting and informative part: (below assumes no guardrails regression) # LB zone UB zone Meaning Decision * 1 > T > T Entire CI above the bar Ship (easy) — confident material win * 2 (0,T) > T Sig positive; CI straddles T Ship if point est ≥ T; else borderline → iterate or ship by risk/cost * 3 (0,T) (0,T) Sig positive; entire CI below T Don't ship as-is — real but not worth the cost; iterate to grow effect (if T≈0 this collapses into a ship) * 4 < 0 > T Straddles both 0 and T — widest/least conclusive Underpowered for the decision: point est > 0 → iterate + higher-powered test; point est < 0 → segment-diagnose harm, then iterate/re-test * 5 < 0 (0,T) Can't rule out harm; even upside can't reach T Weak — best case still below bar; iterate or kill * 6 < 0 < 0 Entire CI below 0 Kill — confidently harmful

Share a time when you had to translate a highly complex mathematical or data science concept to a non-technical executive to secure project buy-in.

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