Tag Archives: Statistics & Econometrics

Publishing ‘hacked’ p-values — the chocolate hoax  [embedded content]

Revisiting the foundations of randomness and probability Regarding models as metaphors leads to a radically different view regarding the interpretation of probability. This view has substantial advantages over conventional interpretations … Probability does not exist in the real world. We must search for her in the Platonic world of ideals. We have shown that the interpretation of probability as a metaphor leads to several substantial changes in...

Significance testing and the real tasks of social science After having mastered all the technicalities of regression analysis and econometrics, students often feel as though they are masters of the universe. I usually cool them down with required reading of Christopher Achen’s modern classic Interpreting and Using Regression. It usually gets​ them back on track again, and they understand that no increase in methodological sophistication … alter the...

Econometric beasts of bias In an article posted earlier on this blog — What are the key assumptions of linear regression models? — yours truly tried to argue that since econometrics doesn’t content itself with only making ‘optimal’ predictions,” but also aspires to explain things in terms of causes and effects, econometricians need loads of assumptions — and that most important of these are additivity and linearity. Let me take the opportunity to elaborate...

Econometrics — the path from cause to effect [embedded content] In their book — Mastering ‘Metrics: The Path from Cause to Effect — Joshua D. Angrist and Jörn-Steffen Pischke write: Our first line of attack on the causality problem is a randomized experiment, often called a randomized trial. In a randomized trial, researchers change the causal variables of interest … for a group selected using something like a coin toss. By changing circumstances randomly,...

Random walks (student stuff)  [embedded content]

Pólya urn models mathematics  [embedded content]

The limits of probabilistic reasoning Almost a hundred years after John Maynard Keynes wrote his seminal A Treatise on Probability (1921), it is still very difficult to find statistics books that seriously try to incorporate his far-reaching and incisive analysis of induction and evidential weight. The standard view in statistics — and the axiomatic probability theory underlying it — is to a large extent based on the rather simplistic idea that more is...

Bayesian statistics — an introduction If you look for a Bayesian statistics textbook accessible also to students with limited knowledge of statistics and math — this is the book!

Machine learning — getting results that are completely wrong Machine-learning techniques used by thousands of scientists to analyse data are producing results that are misleading and often completely wrong. Dr Genevera Allen from Rice University in Houston said that the increased use of such systems was contributing to a “crisis in science” … The data sets are very large and expensive. But, according to Dr Allen, the answers they come up with are likely to...