Headline grabbing effect sizes with sexy variables in complicated models are, over the long run, far more likely to be found wanting than vindicated as possibly right. Science progresses with small, incremental contributions to our knowledge base.

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We characterize our human experience with uncertainty and with variance. Don’t expect anything better from data science on that human experience.

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Weak instruments are a problem in any method dealing with endogeneity where an instrument varible is a proxy for random selection. Heckman selection models share a similar problem of weak instruments, and it has to do with the exclusion restriction.

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In the case of logit models with continuous predictors,it takes some extra work to make sense of and really get a handle on the relationship the predictors and the outcome. Marginal effects and predicted probabilities are, to me, a must have in logit model analysis, particularly with continuous predictors.

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I’m a big fan of logit models. Because we can turn the results of a logit model into a set of predicted probabilities, they let us answer questions that are really interesting to academics and entrepreneurs.

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What limits our impact on management practice is a lack of rigor, and not an excess of it.

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The bottom line is that there is no substitute for using your own judgement when evaluating a study. Ask yourself just how likely it is that the null hypothesis is to be true, particularly when evaluating research purporting to offer surprising, novel, and counterintuitive findings.

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I am actually a big fan of theory; I amm just not wild about the ways in which we (management and entrepreneurship scholars) test it. The driving reason is theoretical looseness; the ability to offer any number of theoretical explanations for a phenomenon of interest.

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In writing decision letters as a journal editor, I find that I am often making similar observations regarding reported empirics in a study, so I thought I would crystalize my five primary guidelines for evaluating a study.

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I am a fan of John Ioannidis and his work. He’s done a lot to raise attention to the use and abuse of frequentist statistics in, well, lots of the sciences.

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