Tag Archives: Statistics & Econometrics

Trösklar och statistisk signifikans I en artikel på Ekonomistas argumenterar nationalekonomen Robert Östling för att lösningen på den uppmärksammade ‘replikationskrisen’ är att ändra på tröskeln för vad som ska betraktas som ‘statistiskt signifikant’ från 5% till 0,5%. Även om detta i sig är vällovligt är det dock ingen lösning. Det räcker inte med att ändra godtyckliga nivåer för vad som ska anses vara ‘statistiskt signifikant’ eller ej. Det är inte där...

The difference between statistical and causal assumptions There are three fundamental differences between statistical and causal assumptions. First, statistical assumptions, even untested, are testable in principle, given sufficiently large sample and sufficiently fine measurements. Causal assumptions, in contrast, cannot be verified even in principle, unless one resorts to experimental control. This difference is especially accentuated in Bayesian...

Why statistics does not give us causality If contributions made by statisticians to the understanding of causation are to be taken over with advantage in any specific field of inquiry, then what is crucial is that the right relationship should exist between statistical and subject-matter concerns … Where the ultimate aim of research is not prediction per se but rather causal explanation, an idea of causation that is expressed in terms of predictive power —...

Why attractive people you date tend​ to be jerks Have you ever noticed that, among the people you date, the attractive ones tend to be jerks? Instead of constructing elaborate psychosocial theories, consider a simpler explanation. Your choice of people to date depends on two factors, attractiveness and personality. You’ll take a chance on dating a mean attractive person or a nice unattractive person, and certainly a nice attractive person, but not a mean...

Substantive relevance — not ‘clever’ design — is what matters most in science  [embedded content] If anything, Snow’s path-breaking research underlines how important it is not to equate science with statistical calculation. And that the value of ‘as-if’ random interventions and experiments ultimately depend on the degree to which they if shed light on substantive and interesting scientific questions. All science entail human judgement, and using statistical...

Causal inference and the rhetoric of imaginary populations (wonkish) The most expedient population and data generation model to adopt is one in which the population is regarded as a realization of an infinite super population. This setup is the standard perspective in mathematical statistics, in which random variables are assumed to exist with fixed moments for an uncountable and unspecified universe of events … This perspective is tantamount to assuming a...

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]