Table of contents

  1. Choosing the correct chart
  2. Choice of data
  3. Comparing data sets – shared horizons
  4. Comparisons of magnitude (size)
  5. Time series
  6. Bar charts for time series
  7. Line charts for time series
  8. Dot plot with line for time series
  9. Multiple time series
  10. Small changes over time, not starting the y-axis at zero
  11. Ranking
  12. Part-to-whole
  13. Deviation
  14. Distribution
  15. Correlation

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Correlation

Correlation charts are often associated with causality and they should be used with caution.

Correlation can show:

  • increases with
  • relates to
  • changes with
  • varies with
  • caused by

Anscombe’s Quartet

Anscombe’s quartet is a powerful illustration of the drawback of relying solely on basic descriptive statistics to summarise data. The data in all four of the graphs in the quartet are virtually identical when using standard descriptive methods. Looking at your data before analysing it is something that Anscombe was passionate about.

Example of Anscombe’s Quartet

A scatter and line chart showing an example of a simple, linear relationship between data.A scatter and plot chart showing an example of a non-linear relationship between data.

A scatter and line chart showing an example of a mostly linear relationship between data, with one outlier.A scatter and line chart showing an example of where there doesn't seem to be a relationship between the data, other than one high outlier.

We are constantly improving based on research and best practice. Any significant changes to our guidance are available on the Updates page.