Entrevista com Larry Cahoon, estatístico do Censo norte-americano. Destaco a passagem abaixo, em que ele ressalta a importância de se saber sobre a variabilidade de uma estimativa, algo tão ou mais crítico do que saber a própria estimativa. Isto está em linha com o que discutimos acerca da acurácia das variáveis econômicas, aqui, aqui e aqui.
To do good statistics, knowledge of the subject matter it is being applied to is critical. I also learned early on that issues of variance and bias in any estimate are actually more important than the estimate itself. If I don’t know things like the variability inherent in an estimate and the bias issues in that estimate, then I really don’t know very much.
A favorite saying among the statisticians at the Census Bureau where I worked is that the biases are almost always greater than the sampling error. So my first goal is always to understand the data source, the data quality and what it actually measures.
But, I also still have to make decisions based on the data I have. The real question then becomes given the estimate on hand, what I know about the variance of that estimate, and the biases in that estimate, what decision am I going to make.
Se você não tinha seguido a recomendação de acompanhar o blog do Damodaran, seguem alguns posts interessantes que você perdeu:
– Twitter announces the IPO: Pricing Games Begins, The Valuation, Why a good trade be a bad investment (or vice-versa).
Sobre o prêmio Nobel, saiu tanta coisa na internet que inclusive descobri muitos detalhes interessantes dos trabalhos dos três ganhadores que sequer imaginava. Deixo aqui, para quem ainda não leu, os materiais do Marginal Revolution e do Cochrane.
Finally, we call attention to one additional aspect of the preceding analysis which may be of interest to teachers of mathematics. This is the fact that our result provides a handy counterexample to some of the stereotypes which non-mathematicians believe mathematics to be concerned with.
Most mathematicians at one time or another have probably found themselves in the position of trying to refute the notion that they are people with “a head for figures.” or that they “know a lot of formulas.” At such times it may be convenient to have an illustration at hand to show that mathematics need not be concerned with figures, either numerical or geometrical. For this purpose we recommend the statement and proof of our Theorem 1. The argument is carried out not in mathematical symbols but in ordinary English; there are no obscure or technical terms. Knowledge of calculus is not presupposed. In fact, one hardly needs to know how to count. Yet any mathematician will immediately recognize the argument as mathematical, while people without mathematical training will probably find difficulty in following the argument, though not because of unfamiliarity with the subject matter.
What, then, to raise the old question once more, is mathematics? The answer, it appears, is that any argument which is carried out with sufficient precision is mathematical, and the reason that your friends and ours cannot understand mathematics is not because they have no head for figures, but because they are unable [or unwilling, DRH] to achieve the degree of concentration required to follow a moderately involved sequence of inferences. This observation will hardly be news to those engaged in the teaching of mathematics, but it may not be so readily accepted by people outside of the profession. For them the foregoing may serve as a useful illustration.
O Noah Smith também aproveita o tema para desenvolver um pouco sobre a matemática e a economia.
Bacana, o prêmio Nobel vai para dois autores de teoria dos jogos, Alvin Roth e Lloyd Shapley!
Na lista de blogs à direita, você encontrará um chamado Market Design, cujo autor é o Alvin Roth. Tendo em vista a notícia, o post de hoje é de que talvez o blog se atrase – mais do que merecido!