Tag Archives: Statistics & Econometrics

Does drinking cause you to become a man? Breaking news! Using advanced multiple nonlinear regression models similar to those in recent news stories on alcohol and dairy and more than 3.6M observations from 1997 through 2012, I have found that drinking more causes people to turn into men! Across people drinking 0-7 drinks per day, each drink per day causes the drinker’s probability of being a man to increase by 10.02 percentage points (z=302.2,...

Laplace’s rule of succession and Bayesian priors After their first night in paradise, and having seen the sun rise in the morning, Adam and Eve was wondering if they were to experience another sunrise or not. Given the rather restricted sample of sunrises experienced, what could they expect? According to Laplace’s rule of succession, the probability of an event E happening after it has occurred n times is p(E|n) = (n+1)/(n+2). The probabilities can be...

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P-values vs. Bayes factors (student stuff) .[embedded content]

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Causal identification requires nonstatistical information in addition to information encoded as data or their probability distributions … This need raises questions of to what extent can inference be codified or automated (which is to say, formalized) in ways that do more good than harm. In this setting, formal models – whether labeled ‘‘causal’’ or ‘‘statistical’’ – serve a crucial but limited role in providing hypothetical scenarios that establish what would be the case if...

How scientists manipulate research All science entails human judgment, and using statistical models doesn’t relieve us of that necessity. Working with misspecified models, the scientific value of significance testing is actually zero — even though you’re making valid statistical inferences! Statistical models and concomitant significance tests are no substitutes for doing real science. In its standard form, a significance test is not the kind of ‘severe...

Analytical strategies in mediation analysis Case 3: Does cognitive skill (M) mediate the relation between college attendance (Z) and earnings (Y)? Case 3 involves one causal variable — college attendance — and two outcomes — one proximal (cognitive skill) and the other distal (earnings). College attendance is causal because a person may or may not go to college. Cognitive skill is not causal because one cannot be assigned or choose to have high skill; it is...