Jun 11, 2026 · 36 min · 11 segments
Good policy depends on evidence — but the statistical methods behind the best research are complex, and few policy-makers can master them in depth. So how do we equip people to engage critically with…
Julia de RomémontGuest
Alan RenwickHost
So this module was in response to the fact that at this point we teach our master's students an intro to quantitative methods, where we teach them both a little bit the methods, but a lot of the coding as well.
And what we've realised over the time is that only a minority of those students actually take to it and end up doing more advanced methods and actually applying it.
And while obviously it makes sense to understand how the methods work to be able to then, you know, read evidence, often it doesn't cover enough ground for them to be actually able to do that effectively once they have a job where they're presented with a lot of evidence.
And also a lot of the time is taken over by the coding and things that also some students feel very stressed about and therefore like don't engage as much as we would want to.
And the discussions that me and colleagues had about this is just like, how can we cover more ground and really focus on the intuitions rather than the mechanics of how these methods work? Because ultimately, you don't need to understand the mechanics in order to assess whether a given piece of evidence is making a credible leap of faith or not.
Because as much as some of these methods are extremely advanced, they're still are trying to make a leap of faith in terms of what they're claiming, right? They're all going to be wrong in some way at describing the very complex social reality.
They have to decomplexify, sorry, that's a very weird word that I chose to say in a podcast, decomplexify reality.
And so I think it's like really focusing on those intuitions that we wanted to build the module around or that I wanted to build a module around.
So what are you imagining are the kinds of situations in which the material that people are covering in this module might turn out to be helpful?
Yes, so especially in the UK government, there's been a big push for doing evidence-based policymaking.
So that means that there's a lot of impact evaluations that are produced by different parts of ministries and so on.
And some of these people are themselves data scientists and know how to do the analysis.
But ultimately, they then bring that evidence forward to... other parts of the government who themselves like other people in the civil service or higher ups or politicians themselves who then need to make decisions based on those things and a lot of our students who do a mass in public policy or mass in international public policy they might end up in these kinds of positions where they're presented with these reports and have to make decisions based on them And the danger often, especially when we talk about quantitative evidence, which this module focus on slightly more or quite a bit more because that's more my personal expertise than qualitative methods, is that there's a danger that like, oh, there's numbers, it's math, it's subjective, and I can just believe what comes out of it.
But there's actually a lot of things that we need to question around that.
And it's basically giving students the confidence and the tools to know where to look without having to look at the mathematical formula in those.
And so the idea that this would then empower them to be able to make decisions even though they feel that they haven't understood the depths of the methods.
And you were suggesting there that this may be useful for students if they go on
But presumably also it can be useful if they themselves become politicians or journalists, commentators.
And, you know, a lot of maybe people like us are often quite kind of disparaging about politicians and the degree to which politicians actually understand the evidence that is before them.
But I guess part of the point of this module is that, yes, those politicians, it would be great if they did have the confidence to engage with this.
So this module was in response to the fact that at this point we teach our master's students an intro to quantitative methods, where we teach them both a little bit the methods, but a lot of the coding as well.
And what we've realised over the time is that only a minority of those students actually take to it and end up doing more advanced methods and actually applying it.
And while obviously it makes sense to understand how the methods work to be able to then, you know, read evidence, often it doesn't cover enough ground for them to be actually able to do that effectively once they have a job where they're presented with a lot of evidence.
And also a lot of the time is taken over by the coding and things that also some students feel very stressed about and therefore like don't engage as much as we would want to.
And the discussions that me and colleagues had about this is just like, how can we cover more ground and really focus on the intuitions rather than the mechanics of how these methods work? Because ultimately, you don't need to understand the mechanics in order to assess whether a given piece of evidence is making a credible leap of faith or not.
Because as much as some of these methods are extremely advanced, they're still are trying to make a leap of faith in terms of what they're claiming, right? They're all going to be wrong in some way at describing the very complex social reality.
They have to decomplexify, sorry, that's a very weird word that I chose to say in a podcast, decomplexify reality.
And so I think it's like really focusing on those intuitions that we wanted to build the module around or that I wanted to build a module around.
So what are you imagining are the kinds of situations in which the material that people are covering in this module might turn out to be helpful?
Yes, so especially in the UK government, there's been a big push for doing evidence-based policymaking.
So that means that there's a lot of impact evaluations that are produced by different parts of ministries and so on.
And some of these people are themselves data scientists and know how to do the analysis.
But ultimately, they then bring that evidence forward to... other parts of the government who themselves like other people in the civil service or higher ups or politicians themselves who then need to make decisions based on those things and a lot of our students who do a mass in public policy or mass in international public policy they might end up in these kinds of positions where they're presented with these reports and have to make decisions based on them And the danger often, especially when we talk about quantitative evidence, which this module focus on slightly more or quite a bit more because that's more my personal expertise than qualitative methods, is that there's a danger that like, oh, there's numbers, it's math, it's subjective, and I can just believe what comes out of it.
But there's actually a lot of things that we need to question around that.
And it's basically giving students the confidence and the tools to know where to look without having to look at the mathematical formula in those.
And so the idea that this would then empower them to be able to make decisions even though they feel that they haven't understood the depths of the methods.
And you were suggesting there that this may be useful for students if they go on
But presumably also it can be useful if they themselves become politicians or journalists, commentators.
And, you know, a lot of maybe people like us are often quite kind of disparaging about politicians and the degree to which politicians actually understand the evidence that is before them.
But I guess part of the point of this module is that, yes, those politicians, it would be great if they did have the confidence to engage with this.
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