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EconTalk

Andrew Gelman on Social Science, Small Samples, and the Garden of the Forking Paths

EconTalk

Library of Economics and Liberty

Economics, Social Sciences, Society & Culture, Education, Science, Books, Philosophy, History, Ethics, Courses, Business, Interviews

4.74.4K Ratings

🗓️ 20 March 2017

⏱️ 68 minutes

🧾️ Download transcript

Summary

Statistician, blogger, and author Andrew Gelman of Columbia University talks with EconTalk host Russ Roberts about the challenges facing psychologists and economists when using small samples. On the surface, finding statistically significant results in a small sample would seem to be extremely impressive and would make one even more confident that a larger sample would find even stronger evidence. Yet, larger samples often fail to lead to replication. Gelman discusses how this phenomenon is rooted in the incentives built into human nature and the publication process. The conversation closes with a general discussion of the nature of empirical work in the social sciences.

Transcript

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0:00.0

Welcome to Econ Talk, part of the Library of Economics and Liberty.

0:08.5

I'm your host, Russ Roberts of Stanford University's Hoover Institution.

0:13.1

Our website is econtalk.org, where you can subscribe, comment on this podcast, and find

0:18.1

links and other information related to today's conversation.

0:21.0

You'll also find our archives where you can listen to every episode we've ever done going

0:25.3

back to 2006.

0:27.5

Our email address is mailadycontalk.org.

0:30.0

We'd love to hear from you.

0:31.7

Today is March 6, 2017, and my guest is author and blogger Andrew Gelman, professor of

0:39.5

statistics and political science at Columbia University.

0:44.8

Andrew's a very dangerous man for me.

0:46.7

As a listers know, I've been increasingly skeptical over the years about the reliability

0:51.6

of various types of statistical analyses and psychology, economics, epidemiology, and

0:57.0

coming across your work, Andrew, which I've done lately and been reading your blog.

1:02.2

You've confirmed a lot of my biases, which is always a little bit dangerous, but in such

1:06.2

an interesting way.

1:08.2

So I'm hoping to learn a lot in this conversation along with our listeners, and at the end,

1:13.6

we'll talk about whether I've gone too far and become too comfortable.

1:18.1

So I want to start with something very, and at the other point I want to make before we

1:21.8

start is that it's sometimes hard to talk about statistics and data over the phone.

1:27.0

And in a podcast, we're going to do the best we can without a whiteboard.

1:31.7

But I'm hoping that both beginners and sophisticated users of statistics will find things of interest

...

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