A Ler

Sobre a adulteração de estudos ditos “científicos” e os seus efeitos.

You’re a social scientist with a hunch: The U.S. economy is affected by whether Republicans or Democrats are in office. Try to show that a connection exists, using real data going back to 1948. For your results to be publishable in an academic journal, you’ll need to prove that they are “statistically significant” by achieving a low enough p-value.

False-Positives, p-Hacking, Statistical Power, and Evidential Value

The Extent and Consequences of P-Hacking in Science

A focus on novel, confirmatory, and statistically significant results leads to substantial bias in the scientific literature. One type of bias, known as “p-hacking,” occurs when researchers collect or select data or statistical analyses until nonsignificant results become significant. Here, we use text-mining to demonstrate that p-hacking is widespread throughout science. We then illustrate how one can test for p-hacking when performing a meta-analysis and show that, while p-hacking is probably common, its effect seems to be weak relative to the real effect sizes being measured. This result suggests that p-hacking probably does not drastically alter scientific consensuses drawn from meta-analyses.

Scientific method: Statistical errors

P values, the ‘gold standard’ of statistical validity, are not as reliable as many scientists assume.

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One thought on “A Ler

  1. O primeiro artigo é uma maravilha, vou partilhar no facebook (só este , os outros são muito mais pesados para quem não tem tempo de ler), faço referência ao seu blogue, claro, mas quero mesmo só partilhar o primeiro 🙂

    Gostar

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