## What is a Nominal p-value?

Aliases: *nominal significance*

The nominal p-value is a calculated observed significance based on a given statistical model. When the statistical model reflects the actual test performed the nominal and actual p-value coincide. When the model is inadequate the nominal and actual significance can differ by varying amounts and oftentimes it is not possible to calculate the actual difference.

The nominal p-value may become a meaningless number if the assumptions of the statistical model used to compute it does not hold. These vary from simple things such as performing the calculation with a predefined (fixed) sample size (no peeking) to more complex ones such as requirements about normal distribution of the error, independence and identical distribution of observations, lack of multiple comparisons / multiple testing, and so on. Violating any of the prerequisites of a significance test will render the nominal p-value more or less non-actionable.

## Related A/B Testing terms

## Articles on Nominal p-value

Like this glossary entry? For an in-depth and comprehensive reading on A/B testing stats, check out the book "Statistical Methods in Online A/B Testing" by the author of this glossary, Georgi Georgiev.