Tag Archives: Statistics & Econometrics

How to think about statistics  [embedded content] If anything, Gelman’s talk underlines how important it is not to equate science with statistical calculation. All science entail human judgement, and using statistical models doesn’t relieve us of that necessity. Working with misspecified models, the scientific value of statistics is actually zero — even though you’re making valid statistical inferences! Statistical models are no substitutes for doing real...

Data without theory is always treacherous Data without theory can lead to bogus inferences … Before being comforted or alarmed, consider whether it makes sense to extrapolate. Is there a persuasive reason why the future can be predicted simply by looking at the past? Or is that wishful thinking? Or nothing at all? … Remember that even random flips can yield striking, even stunning, patterns that mean nothing at all … A statistical comparison of two things...

Monte Carlo simulations and NHST In many social sciences p-values and null hypothesis significance testing (NHST) are often used to draw far-reaching scientific conclusions — despite the fact that they are as a rule poorly understood and that there exist altenatives that are easier to understand and more informative. Not the least using confidence intervals (CIs) and effect sizes are to be preferred to the Neyman-Pearson-Fisher mishmash approach that is so...

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]