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Data Science & Analytics vocabulary · Reporting to Stakeholders

Caveat

Meaning in data science & analytics, with examples

/ˈkæviæt/nounCommon term

In data science & analytics, caveat means: A warning or limitation that the audience should keep in mind when using a result, such as a small sample, missing data or a recent tracking change.

In Turkish: çekince, uyarı, dikkat edilmesi gereken nokta

How it is used

Analysts add caveats to reports and presentations to be honest about uncertainty: 'One caveat is…', 'with the caveat that…'. Too many caveats can hide the main message, so experienced analysts list only the important ones.

Example sentences

  1. One caveat: the March data only covers three weeks because of the tracking outage.

  2. The results look good, with the caveat that the test ran during a holiday week.

Common collocations

  • one caveat is
  • with the caveat that
  • add a caveat
  • important caveat
  • key takeawayThe most important point or conclusion that the audience should remember from a presentation, report or meeting.
  • stakeholderAny person or team with an interest in a piece of work and its results, such as the managers, product owners or clients who will use an analysis.
  • sanity checkA quick, simple test to make sure a result is reasonable before you trust it, for example checking that a total matches a figure you already know.
  • confidence intervalA range of values, calculated from sample data, that is likely to contain the true value; a 95% interval comes from a method that captures the true value 95% of the time.

This word in other fields

The same English word can have a different meaning in another field:

More terms: Reporting to Stakeholders

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