#  The Seven Deadly Selection Biases 

 



####  calendar\_today Date and Time 

 **March 5, 2018** 

 01:00PM - 02:30PM EST 

####  pin\_drop Location 

 **Waterhouse Room, Gordon Hall, 1st. Fl., 25 Shattuck Street**  



 

 [ REGISTER arrow\_circle\_right ](https://selection-biases-3-5-18.eventbrite.com) 

 



 

**[REGISTER](https://selection-biases-3-5-18.eventbrite.com)**  
*This event is part of the Research, Rigor & Reproducibility Series*  
**Speaker:** Xiao-Li Meng, PhD, Dean, Graduate School of Arts and Sciences, and the Whipple V.N. Jone Professor of Statistics, Harvard University *(on sabbatical 2017-2018 academic year)* ([full bio here](https://gsas.harvard.edu/person/xiao-li-meng))  
**Description:** This talk provides a statistical perspective on the roles the seven S’s (sins?) play in increasing the amount of irreproducible research, in medical and life sciences and beyond: 1. Selections in hypotheses (e.g., subgroup analysis);
2. Selections in data (e.g., deleting “outliers” or only using “complete cases”);
3. Selections in methodologies (e.g., for goodness of fit);
4. Selections in due diligence and debugging (e.g., triple checking only when the outcome seems undesirable);
5. Selections in publication (e.g., only when p-value <0.05);
6. Selections in reporting/summary (e.g., suppressing caveats);
7. Selections in understanding and interpretation (e.g., our preference for deterministic, “common sense” interpretation).

 The *Big Data Paradox* and *Simpson’s Paradox* will be used to demonstrate that the problem of irreproducible research is getting BIGGER with Big Data. A cocktail treatment approach together with a *selfish/blowfish test* is suggested to combat this problem.



 

 



 

 

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