Meta-analysis, p-curve, p-uniform… p-tastic!

If you open a Borenstein class on YouTube, you may end up on comments like this :

“A meta-analysis is NOT science.  It is meaningless garbage for people too lazy and too stupid to do their own randomized clinical study.”

Don’t get me wrong, this is more interesting that it seems at first glance. One of the goal of empirical science is actually to estimate effect size and so the first sentence does not make any sense of course. The second part of the second sentence comes from a hater that probably spent countless hours doing interesting and costly clinical studies that failed to reach significance (welcome to Science dude). But the first part of the second sentence is interesting as it contains the adjective “meaningless” and it is true that, recently, medical and social sciences are received severe critics from top experts (including the famous « Why Most Published Research Findings Are False ») concluding that results of many findings and thus of meta-analysis may be really difficult to interpret if not uninterpretable.

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“Improving Your Statistical Inferences” (I)

Daniel Lakens has just started a MOOC in Coursera to share his view on statistical inferences. I will keep you in suspense by not unveiling all the mystery of the class… But I wish I could have followed such a class in Bachelor because, to my view, slight improvements of your statistical and methodological skills can change drastically the way you produce inferences (even tough, ideally, we should understand not only conceptually but technically every tool we’re using to defend strong claims…).

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Migrants : sentiment d’identité ou d’appartenance ?

“La récente visite du chef de l’État à Calais, dans ce qui est communément et ignoblement dénommé la jungle (associant ainsi implicitement les migrants au pire à des bestiaux, au mieux à un peuple reculé qui, ipso facto, ne voudrait pas s’intégrer) a relancé la question de leur répartition sur le territoire national. Pour ceux désirant restés en France, le mouvement sera d’ampleur et mobilisera sans doute plusieurs centaines de communes si l’on veut éviter l’arrivée d’un groupe trop important dans un même endroit. On énonce constamment le sort des accueillants, mais qu’en est-il des accueillis ? Quels facteurs psychologiques permettent de prédire une intégration réussie ? Que disent les études internationales à ce sujet ?”

http://www.la-croix.com/Debats/Forum-et-debats/Migrants-sentiment-d-identite-ou-d-appartenance-2016-10-10-1200795156

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A Priori v. Post Hoc Testing

Vance Berger proposed to consider the problem of post hoc testing via a deck of 52 cards game. If H0 is true, all 52 cards have the same chance to be selected (1/52) and then, if H1 is that the ace has a probability of 1 to show up, then observing a particular ace would lead to a p value of 1/52. Based on a simple case yet, the problem of “inherent multiplicity” occurs.

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Why girls read better than boys and FOX is not THAT bad

Dianne Cook and her colleagues recently published an article in the Annual Review (http://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-041715-033420?src=recsys&) on the different ways to implement statistical graphics in Big Data. Since the 70 and Tukey’s work, exploratory data analysis (EDA) became common among social scientists. Contrary to classical statistical inference, EDA do not necessary require to have specific hypothesis due largely to the enormous amount of data, though this type of analysis can lead later to more formal hypothesis testing. I must confess this preliminary step seduces me much more than generating hypothesis from a random hypothesis generator (which can however be really efficient to show off in society). Let me add that sometimes you can end up learning really different matters if you chose EDA vs. modeling or prediction-based experiments, and I think the Piketty (EDA) vs. Tirole’s (analytical modeling) fight over the new French’s labor law give a good example on how different school can lead to opposite conclusions.

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Why the mean is the best estimator in linear regression?

It might sounds obvious, at first glance, to use mean in most of linear regression we are using and treated outliers in order to avoid the pitfalls of this measure. The use of linear regression has become so mainstream in human and social sciences that this practice seems cast in stone. However, the initial reasons of this is quite straightforward and simple but somehow unknown to some of us.

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ModÉvol (II) : Théorie du signal

Contrairement aux échecs, où toutes les informations sont publiques, les interactions animales et humaines ressemblent beaucoup plus au poker, voire au bridge, où l’accès aux informations et l’éventuelle coopération peut avoir un impact majeur sur le résultat. Et tout comme on peut mentir sur les cartes que l’on possède au poker, et ainsi « bluffer », les signaux mensongers que nous envoyons dans la vie sont en quelque sorte du même acabit informationnel.

Jusqu’à la fin des années 1980, beaucoup de biologistes considéraient que le monde animal et humain était en grande partie fait de signaux peu coûteux et honnête jusqu’à ce que Dawkins et Krebs publient conjointement « Animal signals: informations or manipulation? », tentative de construction d’un modèle montrant qu’au contraire, parce que les animaux partagent des intérêts divergents, les signaux honnêtes devraient être rares. Ils s’opposaient donc frontalement à la théorie du biologiste israélien, Amotz Zahavi, qui, quelques années auparavant, arguaient dans un papier resté célèbre que les caractéristiques coûteuses comme la queue et les ocelles du paon témoignent d’un signal honnête démontrant la qualité du mâle que seul ce dernier pouvait se permettre de produire. A cette époque, la réactance face à cette théorie fut massive puisqu’elle souleva également les doutes de l’éminent John Maynard Smith dans Journal of Theoretical Biology.

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