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Missing value

As explained in Grokking Statistics

Missing data consists of observations or variable values that are absent from a dataset when they are expected or needed.

A missing value is an observation or variable value absent when it is expected or needed. Gaps arise because data-collection tools cannot capture every place, time, or context. section 2.2

The family-photo gap

A handful of family photographs cannot reveal a whole life. Important moments may never be photographed, while the selected images may show a polished, biased version of reality. section 2.2

Treat missingness as a question about what the data can support:

  • Expect gaps in surveys, self-driving cars, weather sensors, and other data-collection systems. section 2.2
  • Narrow the objective before collecting data so the required observations become practical and relevant. section 2.2
  • Account for missing moments and curated observations before drawing conclusions, because gaps can bias inference. section 2.2

How Grokking Statistics covers it

How deep it goes
Explains why missing information occurs and how it can bias inference, but stops short of formal missing-data methods.
Examples
Uses family photographs, surveys, self-driving cars, weather sensors, golf observations, and scorecards.
What you can do after
Evaluate whether collected observations support a focused claim without ignoring gaps or curated evidence.
How it connects
Connects missing values with data collection, sampling bias, inference, and the scientific method.

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This explainer was generated by Manning's knowledge graph from the text of Grokking Statistics. The section links point to the parts of the book it draws on.