Measurement and evidence

Statistical power

The probability that a test will detect a real effect of a given size, if an effect of that size genuinely exists in the population being measured. A design with low power will usually miss a genuine effect, raises the share of the results it does produce that are noise rather than signal, and exaggerates the size of the effects it does detect.
what it means here

In practice, for a UK buyer

The paragraph above is the neutral answer. This is the part a vendor glossary leaves out.

The concept that separates measurement from theatre, and the one almost no media platform surfaces. AdBuyMCP computes it before running a lift test, defaulting to 80% power at 5% significance on daily observations, and refuses to proceed when the design falls short — returning the days of post-exposure data the test would actually need and the minimum lift it could detect. The refusal is the feature: an underpowered result is not a weak answer, it is a random one, and a random number in a board pack is indistinguishable from a real one.

same group

More on measurement and evidence

What you can prove afterwards, and the difference between a number that means something and a number that does not.

All 76 terms

45 minutes. Bring a real brief and we compile it live. You describe one audience and watch it compile into seven targeting specifications, each with the score for how much of the definition survived.

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