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Data

As explained in Grokking Statistics

Data is a collection of recorded observations, measurements, or other information.

Data is recorded measurements or observations collected, ideally, in a systematic way. It is an asset whose usefulness depends on quality, context, and origin—not an automatic source of truth. section 2.1 section 2.2

Data can take several practical forms:

  • Structured data usually appears in tables, with variables in columns and observations in rows, ready for tools such as pandas or Excel. section 2.0
  • Unstructured data includes documents, audio, and images; its many variables often make manual statistical analysis impractical. section 2.0
  • A photograph, transaction record, temperature reading, or exam score is a data point that offers clues without capturing everything about reality. section 2.0 section 2.2
  • Statistics turn large collections of observations into descriptive parameters that reveal patterns more quickly, while interpretation still requires attention to source and bias. section 1.2 section 2.2

The family photo test

A family photograph shows clothing, expressions, a room, and who is visible. It may provide surface-level clues, but it cannot reliably reveal the family’s complete life story: important events remain outside the frame. More data helps only when the additional observations are relevant and trustworthy. section 2.2

Use the figure to make the difference between recorded observations and the larger reality they represent visually concrete. section 2.1

How Grokking Statistics covers it

How deep it goes
Defines data, distinguishes structured from unstructured forms, and explains limits of inference without developing collection methods or formal models.
Examples
Uses digital interactions, photographs, temperatures, exam scores, documents, audio, images, and tabular datasets as examples.
What it assumes
Assumes no prior statistics and teaches the meaning of data before introducing measures, samples, populations, or analysis tools.
What you can do after
Evaluate data as evidence by identifying its form, source, quality, and limits before interpreting patterns or building models.
How it connects
Connects data with statistics, samples, populations, data visualization, machine learning, and data privacy.

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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.