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 … […]
Statistics & Econometrics
The random assignment plus masking are supposed to make it likely that the two groups have the same distribution of causal factors. It is controversial how confident these measures should make us that they do this. This issue bears on the trustworthiness of causal claims backed by RCTs. As we noted, trustworthiness is the central […]
‘Ideally controlled experiments’ tell us with certainty what causes what effects — but only given the right closures. Making appropriate extrapolations from (ideal, accidental, natural or quasi) experiments to different settings, populations or target systems, is not easy. ‘It works there’ is no evidence for ‘it will work here.’ Causes deduced in an experimental setting […]
Modularity is the mark of a type of independence from context. The same functional relationship between variables will hold in a given component of the contributing mechanisms whether or not there is a change in a different component. The total effect may change when different components contribute, but the operation of the modular mechanism will […]
. Yours truly has for several years been conducting a doctoral course in statistics for students in educational science where SPSS has been used. Unfortunately, Bayesian analysis has not been available in that program. With version 29 of SPSS, things have changed. So, next year, there will be a new addition to the course!
Many journal editors request authors to avoid causal language, and many observational researchers, trained in a scientific environment that frowns upon causality claims, spontaneously refrain from mentioning the C-word (“causal”) in their work … The proscription against the C-word is harmful to science because causal inference is a core task of science, regardless of whether […]
. As always a pleasure listening to Edward Leamer and his critical views on the (mis)uses of statistical methods in empirical research. Main message: without a deep understanding of context, statistical and econometric analyses are useless!
. Many economists — yours truly included — are highly sceptical of the ability of mainstream economics to deliver useful models. Some of us even question the ‘modern’ insistence on modelling — “if it’s not in a model, it’s not economics.” Even if we accept the limitation of only being able to say something about […]
Always, but always, plot your data. Remember that data quality is at least as important as data quantity. Always ask yourself, “Do these results make economic/common sense”? Check whether your “statistically significant” results are also “numerically/economically significant”. Be sure that you know exactly what assumptions are used/needed to obtain the results relating to the properties of any estimator or […]