Dr. Neil Dullaghan is a senior research manager at Rethink Priorities. Rethink Priorities is a global priority think-and-do tank, aiming to do good at scale. We research and implement pressing opportunities to make the world better. We act upon these opportunities by developing and implementing strategies, projects, and solutions to key issues. We do this work in close partnership with foundations and impact-focused non-profits or other entities. Neil currently works in the animal welfare team, with an expertise in European Union policy.
Neil is also a fund manager on the EA animal welfare fund.
You can hear my takes here:
He holds a PhD in Political & Social Science from the European University Institute, an MPhil in European Politics & Society from the University of Oxford and a BA in International Relations from Dublin City University.
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 behavioural science think tank, The Decision Lab.
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?