The Hidden Reason Most Data-Driven Decisions Fail

Описание: 🔗 Whitepaper: https://confidentstory.com/docs/white...

Most data-driven decisions don’t fail because of bad analysis.
They fail much earlier... because the data itself is wrong.

In this video, I explain the hidden requirement of data-driven decision making that almost nobody talks about:
clean data.

Using A/B testing as a concrete example, I show how teams believe they’re measuring one thing, while in reality they’re measuring something completely different. When that happens, even statistically significant results can lead to decisions that collapse once rolled out.

If you’ve ever seen this pattern:
an experiment “wins”
the change is shipped
the uplift disappears weeks later

…this video explains why.

Over seven years of running experiments for clients, I kept seeing the same anomaly. It’s often dismissed as reversion to the mean, but that explanation never fully accounted for what was happening. So I went deeper — into how experiment data is collected, not how it’s analyzed.

This video covers:
why clean data is the real bottleneck of data-driven decisions
how A/B tests quietly collect contaminated data
why significance doesn’t protect you from measuring the wrong effect
how decision failures are baked in at the collection stage
-why many “wins” are structurally unstable

This isn’t a critique of statistics.
It’s a critique of measurement.

If your organization relies on experiments to make decisions, this video will change how you think about what your data actually represents.

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