Neil_Dullaghan

Neil is a full-time staff researcher at Rethink Priorities. His work focuses mostly on the EA Survey, Local EA Groups survey, public opinion polling, and political reform covering longterm, shorterm, human and nonhuman animal interventions.

He is currently also a Ph.D. candidate in Political and Social Science at the European University Institute.

He has volunteered for Charity Entrepreneurship & Animal Charity Evaluators. Before joining Rethink Priorities, he was a political data manager for WeVoteUSA while it participated in Fast Forward's accelerator for tech nonprofits, held numerous research assistant positions at the University of Oxford, and acted as Strategy Associate for a behavioral science think tank, The Decision Lab.

Comments

Reducing long-term risks from malevolent actors

Thanks for this!
I see you referenced Matthews et al. (2018), which I haven't read, and wondered if you had also seen the Authoritarian Ruling Elites Database, compiled by  Matthews (2019): “a collection of biographical and professional information on the individuals who constitute the top elite of authoritarian regimes.” Each of the project’s 18 datasets focuses on a particular regime, such as the military dictatorship that ruled Chile from 1973 to 1990. The biographical data-points include gender, occupation, dates of birth and death, tenure among the elite, and more. (This came to me via the Data Is Plural mailing list fwiw). Apologies if you mentioned it and I missed it.
 

EA Forum update: New editor! (And more)

+1 that the footnotes issue is quite an inconvenience. 

EA Survey 2019 Series: Engagement Levels

GWWC membership here refers to anyone who provided a year in response to the question in the EA Survey "If you have taken the Giving What We Can pledge, please note the year:"

EA Survey 2019 Series: Community Demographics & Characteristics

Glad to hear that it is a useful resource!

I have updated the summary to include links to all the posts in the series so far (a pingbacks list will also appear at the bottom of the post if one opts into the experimental features on the Forum). The entire list of articles in the EA Survey 2019 Series (plus our other publications) can also be found on our website.

EA Survey 2019 Series: Geographic Distribution of EAs

Good point! Thanks. I have added FHI to the text.

Should EAs be more welcoming to thoughtful and aligned Republicans?

Since you mentioned it in your footnote, the EA Survey 2019 post on geographic distribution of EAs is out. We don't have information on party identification, but we can see that 2.23% of EAs living in the USA are politically affiliated with the Center Right and 1.19% with the Right (12.76% with Libertarianism & 76.56% with the Left or Center Left). Keeping in mind the caveat that our data only shows where an EA currently lives so an EA reporting both living in the USA and being on the Right-hand side of the political spectrum does not necessarily mean they are a registered Republican.

https://forum.effectivealtruism.org/posts/cvkqyxepf4W2whYSK/ea-survey-2019-series-geographic-distribution-of-eas#Political_affiliation

EA Survey 2019 Series: Cause Prioritization

Hi, thanks.

I agree that "If I have observed a p < .05, what is the probability that the null hypothesis is true?" is a different question than "If the null hypothesis is true, what is the probability of observing this (or more extreme) data”. Only the latter question is answered by a p-value (the former needing some bayesian-style subjective prior). I haven't yet seen a clear consensus on how to report this in a way that is easy for the lay reader.

The phrases I employed (highlighted in your comment) were suggested in writing by Daniel Lakens, although I added a caveat about the null in the second quote which is perhaps incorrect. His defence of the phrase “we can act as if the null hypothesis is false, and we would not be wrong more than 5% of the time in the long run” is the specific use of the word ‘act’, "which does not imply anything about whether this specific hypothesis is true or false, but merely states that if we act as if the null-hypothesis is false any time we observe p < alpha, we will not make an error more than alpha percent of the time". I would be very interested if you have suggestions of a similar standard phrasing which captures both the probability of observing data (not a hypothesis) and is somewhat easy for a non-stats reader to grasp.

As an aside, what is your opinion on reporting p values greater than the relevant alpha level? I've read Daniel Lakens suggesting if you have p< .05 one could write something like "because given our sample size of 50 per group, and our alpha level of 0.05, only observed differences more extreme than 0.4 could be statistically significant, and our observed mean difference was 0.35, we could not reject the null hypothesis’." This seems a bit wordy for any lay reader but would it be worth even including in a footnote?

EA Survey 2019 Series: Cause Prioritization

Hi,

On your first point, yes you are correct. Among those who prioritized Global Poverty OR Animal Welfare AND changed causes, pluralities of them changed to AI.

On your second point, I've now added a column in the group membership and demographics tables that shows the average for the sample as a whole. I hope this helps.

EA Survey 2019 Series: Cause Prioritization

Hi, thanks! We will explore cause prioritization and the geographic distribution of EAs in a forthcoming post. We tried to keep a narrower focus in this post, on involvement in EA and just a few demographics, as we did in last year's post.

EA Survey 2019 Series: Community Demographics & Characteristics

Hi, Glad to hear you found it informative. Thanks! We have an entire post dedicated to the geographic distribution of EAs in this year's survey forthcoming, along the same lines as last year's: https://forum.effectivealtruism.org/posts/t2Wqszc4wpKxMinSs/ea-survey-2018-series-geographic-differences-in-ea-1

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