I am working on a data set, containing demographic informations in a developing country. I noticed that a third of the ages is a multiple of 10 ! I expected (though the distribution of ages is not uniform) that a tenth of the surveyees would have an age that is a multiple of ten.

Here is my question:

  • Is it common in age data to observe such a pattern ?

  • Should I infer that surveyees do not really know their age ?

  • $\begingroup$ Where did you get the data? It might just be that each row describes an age group, rather than individual people. Do you have a description of the dataset? $\endgroup$
    – Adrian
    Commented Apr 22, 2016 at 13:45
  • 2
    $\begingroup$ Yes and yes. (You would likely also see smaller peaks at multiples of five years.) This happens even in the US, especially in more rural counties. What do you want to do about it? $\endgroup$
    – whuber
    Commented Apr 22, 2016 at 13:45
  • $\begingroup$ @whuber I don't plan to do anything, I was just curious... $\endgroup$
    – RUser4512
    Commented Apr 22, 2016 at 13:47
  • 1
    $\begingroup$ If you're using that data set to develop information, observe patterns, make decisions, or anything else of importance then you might want to consider adjusting your results for this added uncertainty (and potential bias). $\endgroup$
    – whuber
    Commented Apr 22, 2016 at 13:48
  • $\begingroup$ While I don't think it's directly relevant to the question at hand, Benford's Law, en.wikipedia.org/wiki/Benford's_law may be of interest to you. $\endgroup$ Commented Apr 22, 2016 at 15:26

1 Answer 1


Perhaps is because the dataset has been anonymized.

In general, datasets that contain personal information about individuals are anonymized before they are released.

That is, the records of the data base are stripped of any personal information (name, ID number, etc), and the demographic attributes (age, address, gender, country of origin, ethnicity) are distorted. By distorted I mean, that some of the attributes are modified (or generalized) such that no person can be undoubtedly linked to a single record in the data base.

Examples of modifying attributes could be remove the last digits of Zip Codes, or round the age to the closest multiple of 10.

An example to note the importance of anonymizing a dataset:

In [P1] it was shown in that 87% of the population in the United States may be unequivocally identified solely on the basis of the triple consisting of their date of birth, gender and 5-digit ZIP code, according to 1990 census data.

Those techniques are called statiscal disclousure control [P2], and there is a lot of literature in this regard. Notice that there is a tension between the protection of the privacy of the records in a dataset and the utility-loss of that dataset.

There are several privacy-measures that protect the privacy of the records of the data set against different kinds of attack that try to unambiguously identify records in the dataset.


  • $\begingroup$ Thank you for explaining a very interesting theory and providing the references. Welcome to our site! $\endgroup$
    – whuber
    Commented Apr 22, 2016 at 16:58

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