# Why are missing values not ignored by default?

**URL:** <https://discourse.julialang.org/t/why-are-missing-values-not-ignored-by-default/106756>\
**Category:** Internals & Design\
**Tags:** data, missing-values\
**Created:** [November 26, 2023, 1:45am UTC](https://discourse.julialang.org/t/why-are-missing-values-not-ignored-by-default/106756 "2023-11-26T01:45:36Z")\
**Posts on this page:** 1\
**Showing post:** 57

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**Author:** ![pdeffebach](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pdeffebach/32/10320_2.png) [@pdeffebach](https://discourse.julialang.org/u/pdeffebach)\
**Post date:** [November 27, 2023, 5:52pm UTC](https://discourse.julialang.org/t/why-are-missing-values-not-ignored-by-default/106756/57 "2023-11-27T17:52:25Z")

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It’s the data analyst’s job to ensure data integrity. The question “which observations contribute to this statistic” is something Julia, the language, can’t answer. The analyst should absolutely conduct additional robustness checks about how missing values are handled and what’s the appropriate way to deal with them.

The question is whether imposing `skipmissing(...)` or propagation on Boolean operations is the the right way to go about that. It’s costly for users to write `skipmissing` every time they wish to calculate the `mean`. I’m simply making an argument that the cost isn’t always worth the benefits.

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