Friday, June 8, 2018

Propagation of Mistakes in Papers

While reading papers on cardinality estimation I noticed something odd: The seminal paper by Flajolet and Martin on probabilistic counting gives a bias correction constant as 0.77351, while a more recent (and very useful) paper by Scheuermann and Mauve gives the constant as 0.775351. Was this a mistake? Or did they correct a mistake in the original paper?

I started searching, and there is a large number of papers that uses the value 0.775351, but there is also a number of papers that uses the value 0.77351. Judging by the number of Google hits for "Flajolet 0.77351" vs. "Flajolet 0.775351" the 0.77351 group seems to be somewhat larger, but both camps have a significant number of publications. Interestingly, not a single paper mentions both constants, and thus no paper explains what the correct constant should be.

In the end I repeated the constant computation as explained by Flajolet, and the correct value is 0.77351. We can even derive one digit more when using double arithmetic (i.e., 0.773516), but that makes no difference in practice. Thus, the original paper was correct.

But why do so many paper use the incorrect value 0.775351 then? My guess is that at some point somebody made a typo while writing a paper, introducing the superfluous digit 5, and that all other authors copied the constant from that paper without re-checking its value. I am not 100% sure what the origin of the mistake is. The incorrect value seems to appear first in the year 2007, showing up in multiple publications from that year. Judging by publication date the source seems to be this paper (also it did not cite any other papers with the incorrect value, as far as I know). And everybody else just copied the constant from somewhere else, propagating it from paper to paper.

If you find this web page because you are searching for the correct Flajolet/Martin bias correction constant, I can assure you that the original paper was correct, and that the value is 0.77351. But you do not have to trust me on this, you can just repeat the computation yourself.

1 comment:

  1. Like in biology, when there is a mutation but without a lot of selection pressure: the error can proliferate: no phenotype extinction, no genotype extinction (of the "wrong" code).