Mal nommer les choses c'est ajouter aux malheurs du monde Albert Camus, 1944
It's damn easy to get wrong using utterly right figures (*)
Lasciate ogne speranza, voi ch'intrate Dante's Inferno, 1313

hypocrisy, scam ... at work everyday

not even counting, and much more often than "fake news"

WashingtonPost: Meta .vs. Facebook

from "fake-news" for everyone to "fake-faces" for each one

GeckoBoard: about statistical-fallacies

sort of 1950's "How to Lie with Statistics" revisit

The Ringer (Mar.26 2021)

About social media alternatives (?)

DirtyDataScience project (2018-2021)

Statistical learning on non-curated data

TowardsDataScience: about M-L (2020)

Machine-Learning going wrong

Guardian: about data-Moguls (Jul. 2020)

US Congress .vs. GAFAs to answer for their misdeeds

Anodot: about data quality (Jun. 2020)

The Price You Pay for Poor Data Quality

Dataversity: about data quality (Apr. 2020)

How Much Data Quality is Good Enough?

Wire: wrong with right (Jun. 2019)

Right Data, Wrong Conclusions: a controversy

Government Technology (Mar. 2017)

When Big Data Gets It Wrong

Science Mag special issue (Feb. 2017)

Why turning data into policy is harder than it sounds

Science Mag news (Feb. 2017)

Clash at NOAA about quality assurance protocols

Science Mag: public data quality (Jan. 2016)

Improving the quality of publicly archived data
Hey, data ! get bigger, don't get
more buggy