Worth reading!

9 November 2017

Beyond subjective and objective in statistics
Andrew Gelman and Christian Hennig

Journal of the Royal Statistical Society: Series A (Statistics in Society)
Volume 180, Issue 4, October 2017, Pages 967–1033

Summary
Decisions in statistical data analysis are often justified, criticized or avoided by using concepts of objectivity and subjectivity. We argue that the words ‘objective’ and ‘subjective’ in statistics discourse are used in a mostly unhelpful way, and we propose to replace each of them with broader collections of attributes, with objectivity replaced by transparency, consensus, impartiality and correspondence to observable reality, and subjectivity replaced by awareness of multiple perspectives and context dependence. Together with stability, these make up a collection of virtues that we think is helpful in discussions of statistical foundations and practice. The advantage of these reformulations is that the replacement terms do not oppose each other and that they give more specific guidance about what statistical science strives to achieve. Instead of debating over whether a given statistical method is subjective or objective (or normatively debating the relative merits of subjectivity and objectivity in statistical practice), we can recognize desirable attributes such as transparency and acknowledgement of multiple perspectives as complementary goals. We demonstrate the implications of our proposal with recent applied examples from pharmacology, election polling and socio-economic stratification. The aim of the paper is to push users and developers of statistical methods towards more effective use of diverse sources of information and more open acknowledgement of assumptions and goals.

Full article: http://onlinelibrary.wiley.com/doi/10.1111/rssa.12276/epdf

 

 

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Parce que c’est en français (sorry, guys).

9782807314917-g

Statistique descriptive

Cheat sheets for R

9 September 2017

Useful “cheat sheets” for R and RStudio

  • Data Import
  • Data Transformation
  • Sparklyr
  • R Markdown
  • RStudio IDE
  • Shiny
  • Data Visualization
  • Package Development

Click me!

Quote

16 September 2016

Good science is the one which disobeys

xkcd: Linear regression

30 August 2016

linear_regression

The 95% confidence interval suggests Rexthor’s dog could also be a cat, or possibly a teapot.

Big data…

29 June 2016

The risks of Big Data – or why I am not worried about brain tumours

http://understandinguncertainty.org/risks-big-data-%E2%80%93-or-why-i-am-not-worried-about-brain-tumours

Link: http://nyti.ms/1XsJPHp

A good and sound idea or a way to avoid scientific critics?

Fools…

15 June 2015

Fools make researches and wise men exploit them. H. G. Wells

About correlation

4 June 2015

An “old” but refreshing paper about the “correlation fallacy”: Anscombe’s Quartet

He presents four bivariate data sets with the same number of observations, same means, same variance, same correlation and same regression coefficients, but…

Look at it: http://www.sjsu.edu/faculty/gerstman/StatPrimer/anscombe1973.pdf

One of my colleague is planning to submit a paper to the International Journal of Eating Disorders and they provide very useful and sound guidelines regarding statistical thinking (indeed, their recommendations are more than just statistical reporting guidelines).

For example:

Misinterpretation of Nonsignificant Hypothesis Tests
A common scientific error is the misinterpretation of a nonsignificant hypothesis test as evidence of no effect. We are taught never to accept a null hypothesis. One can fail to  reject a hypothesis for many reasons, other than no effect. Among these, a study can be underpowered, have unexpectedly large variance, fail to recruit the desired number of participants, have a model with two correlated predictors such that in the presence of the other, neither has any significant prediction of the response, and many other reasons. This situation is analogous to the verdict in a criminal trial in the United States: Guilty or Not Guilty. Not Guilty does not mean innocent. One can be found not guilty because there was insufficient evidence, some evidence was ruled inadmissible by the judge, evidence became contaminated, the prosecutor poorly organized or presented what would have been sufficient evidence, etc. Absence of Evidence is not Evidence of Absence.

Guideline: Never interpret a nonsignificant effect as evidence that no effect exists.
References: Ioannidis J. “Why most published research findings are false”. PLoS Med 2005; 2: e124. doi:10.1371/journal.pmed.0020124. PMID 16060722

You can find the document right here.