Fallacies of Data Science

Adnan Masood and David Lazar have a list of fallacies in the world of data science:

Extrapolating beyond the range of training data, especially in the case of time series data, is fine providing the data-set is large enough.

Strong Evidence is same as a Proof! Prediction intervals and confidence intervals are the same thing, just like statistical significance and practical significance.

These are some good things to think about if you’re getting into analytics.

Related Posts

Road Construction Incentive Contracts And R

Sebastian Kranz promotes an interesting RTutor project: Patrick Bajari and Gregory Lewis have collected a detailed sample of 466 road construction projects in Minnesota to study this question in their very interesting article Moral Hazard, Incentive Contracts and Risk: Evidence from Procurement in the Review of Economic Studies, 2014.They estimate a structural econometric model and find that […]

Read More

Analyzing Customer Churn With Keras And H2O

Shirin Glander has released code pertaining to a forthcoming book chapter: This is code that accompanies a book chapter on customer churn that I have written for the German dpunkt Verlag. The book is in German and will probably appear in February: https://www.dpunkt.de/buecher/13208/9783864906107-data-science.html.The code you find below can be used to recreate all figures and analyses from this […]

Read More


May 2016
« Apr Jun »