The first CREDAM Award for creative data management goes to … the German government!

“If you torture the data long enough, it will confess.”
This aphorism, attributed to Ronald Coase, sometimes has been used in a disrespective manner, as if was wrong to do creative data analysis. This view obviously is misleading. In contrast, we at IRET have a much more positive and humanistic view of data management, and therefore we have made this aphorism to our leading guide in difficult times.

We at IRET have made it to our mission to proliferate and foster creative ways of data analysis. Therefore, we proudly introduce an award in recognition of outstanding data creativity: the CREDAM Award. CREDAM is both an acronym (CREative DAta Management), and a statement: credam (lat.) means “I will believe”, or “I will trust”.

This years CREDAM Award goes to …….. the German government!

A new report on poverty in Germany is going to be published soon. What does the data say?

YearOverall property in possession of rich householdsOverall property in possession of complete lower half
199845%3%
200853%1%

Seems like a pretty clear picture, and in a previous version of the report, the authors concluded (based on this and other data), that “income disparity increased” (see Süddeutsche Zeitung). But that is wrong!! But why is it wrong? Well, that interpretation “does not reflect the opinion of the German government”.

On the pressure of the leader of the minor coalition partner, Philipp Rösler (which currently would be elected by 4% of Germans), this conclusion was re-interpreted. Now, the report comes to the completely opposite conclusion: “income disparity decreases!

As this is a great example of creative data analysis, which liberates us from restrictive and anally retentive “scientific” procedures, we are happy to award the first CREDAM trophy to the German government, especially Phillip Rösler. Congratulations!

(Maybe we should think about adopting this strategy for scientific reports as well. Given highly flexible approaches of data analysis, conclusions should rather be based on a majority vote of all (co-)authors and reviewers, not on empirical evidence.)

Improved evolution of correlations
Further thoughts on post-publication peer review (PPPR)
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