I’ll be talking at the NYU business school, in the department of information, operations, and management sciences, this Fri, 8 Dec 2017, at 12:30, in room KMC 4-90 (wherever that is):

Little Data: How Traditional Statistical Ideas Remain Relevant in a Big-Data World; or, The Statistical Crisis in Science; or, Open Problems in Bayesian Data Analysis

“Big Data” is more than a slogan; it is our modern world in which we learn by combining information from diverse sources of varying quality. But traditional statistical questions—how to generalize from sample to population, how to compare groups that differ, and whether a given data pattern can be explained by noise—continue to arise. Often a big-data study will be summarized by a little p-value. Recent developments in the social and medical sciences have made it clear that our usual statistical prescriptions, adapted as they were to a simpler world of agricultural experiments and random-sample surveys, fail badly and repeatedly in the modern world in which millions of research papers are published each year. Can Bayesian inference help us out of this mess? Maybe, but much research will be needed to get to that point.

Andrew,

I’m salivating over the description, wishing I was in NYC. Any chance will be recorded (even audio) or you can share notes and/or slides?

–Scott

Scott:

I don’t think this one will be recorded but there are a bunch of my talks on youtube; perhaps the most watchable can be found by googling *andrew gelman youtube new york r conference*.

Also, I don’t know if I’ll be using slides in my talk on Friday. But slides from an old version of my Little Data talk are here.

Wish I could make it, but I’m presenting right at the same time in NYU UC-24 (wherever that is). I’m one of many presenters in “NYU 2017 Conference on Digital, Mobile Marketing, and Social Media Analytics”.

I’ll be presenting my work on consequences of a data breach on users behavior. Bottom line – average null effect – users didn’t seem to change their behavior. But there is interesting heterogeneity.

I incorporate much of what I learn from your blog – being very cautious in my analyses. And damn, that’s hard; it’s much easier to p-hack :-)

Will this talk be open to the public?

Yes, I assume so.