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Tag Archives: Statistics & Econometrics

On probabilism and statistics

On probabilism and statistics ‘Mr Brown has exactly two children. At least one of them is a boy. What is the probability that the other is a girl?’ What could be simpler than that? After all, the other child either is or is not a girl. I regularly use this example on the statistics courses I give to life scientists working in the pharmaceutical industry. They all agree that the probability is one-half. So they are all wrong. I haven’t said that the older...

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What should we do with econometrics?

What should we do with econometrics? Econometrics … is an undoubtedly flawed paradigm. Even putting aside the myriad of technical issues with misspecification and how these can yield results that are completely wrong, after seeing econometric research in practice I have become skeptical of the results it produces. Reading an applied econometrics paper could leave you with the impression that the economist (or any social science researcher) first formulated...

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Deaton-Cartwright-Senn-Gelman on the limited value of randomization

Deaton-Cartwright-Senn-Gelman on the limited value of randomization In Social Science and Medicine (December 2017), Angus Deaton & Nancy Cartwright argue that RCTs do not have any warranted special status. They are, simply, far from being the ‘gold standard’ they are usually portrayed as: Randomized Controlled Trials (RCTs) are increasingly popular in the social sciences, not only in medicine. We argue that the lay public, and sometimes researchers, put...

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The thing that people just don’t get about statistics

The thing that people just don’t get about statistics The thing that people just don’t get is that is just how easy it is to get “p less than .01” using uncontrolled comparisons … Statistics educators, including myself, have to take much of the blame for this sad state of affairs. We go around sending the message that it’s possible to get solid causal inference from experimental or observational data, as long as you have a large enough sample size and a...

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Texas sharpshooter fallacy

Data clusters are everywhere, even in random data. Someone who looks for an explanation will inevitably find one, but a theory that fits a data cluster is not persuasive evidence. The found explanation needs to make sense, and it needs to be tested with uncontaminated data. Similarly, someone who fires enough bullets at enough targets is bound to hit​ one. A researcher who tests hundreds of theories is bound to find evidence supporting at least one. Such evidence is...

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Empirical economics and statistical power

Empirical economics and statistical power How credible is empirical economics? Is empirical economics adequately powered? Many suspect that statistical power is routinely low in empirical economics. However, to date, there has been no large-scale survey of statistical power widely across empirical economics. The main objectives of this article are to fill this gap, investigate the implications of low power on the magnitude of likely bias and recommend...

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Meta-analysis — nothing but an exercise in mega-silliness

Meta-analysis — nothing but an exercise in mega-silliness Including all relevant material – good, bad, and indifferent – in meta-analysis admits the subjective judgments that meta-analysis was designed to avoid. Several problems arise in meta-analysis: regressions are often non -linear; effects are often multivariate rather than univariate; coverage can be restricted; bad studies may be included; the data summarised may not be homogeneous; grouping...

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