Send us Fan Mail In this episode of The Science Intersection, I’m joined by Professor Michael Sanders and Julia Ellingwood for a conversation about evidence-based policy — what it means, why it matters, and why good ideas do not always translate neatly into better outcomes. Michael is Professor of Public Policy at King’s College London. His work focuses on evidence, behavioural change, and how research can be used to improve public services and policy. Julia Ellingwood is a research fellow at the Policy Institute at King’s College London. Her work looks at how evidence and practical interventions can help improve outcomes in the real world, and her PhD focuses on wellbeing in the UK civil service. In Part 1, we talk about how evidence-based policy can help public money, professional time and people’s lives be taken seriously. We discuss why some ideas that seem obvious — like homework or reducing class sizes — may be more complicated once you look at the evidence. Julia explains why evidence-based policy does not mean stopping every policy until there is a perfect trial, but instead using evidence as a set of tools to work out what we know, what we do not know, and what we most need to test. We also look at why social policy is often harder to test than medicine, what researchers can do when a randomized trial is not possible, and how governments can act under uncertainty while still building evidence into the process. Later in the episode, we move into the relationship between evidence, ideology and democracy. Michael discusses examples including Sure Start, austerity, the Rwanda scheme and synthetic phonics, and we ask where evidence can cut through political disagreement — and where values and ideology still matter. The episode ends by looking at citizens’ assemblies, including how they can help people work through contested issues when they are informed by evidence rather than simply being a space for opinion. Listen to more from Michael on his podcast here: https://open.spotify.com/show/3Y7RSs7pBOC7hlafpGVR1G?si=85d114fba4514b27 and read the creative commons book here: https://www.kcl.ac.uk/sfg/assets/designing-and-delivering-randomised-trials-for-social-policy-digital.pdf Glossary Evidence-based policy Making policy using the best available evidence, rather than just instinct, ideology or tradition. RCT / randomized controlled trial A test where people, schools, areas or organisations are randomly put into groups, so you can compare those who got the intervention with those who did not. Randomise / randomization Choosing who gets the policy or intervention by chance, so the groups are as fair and comparable as possible. Intervention The thing being tested — for example, a new service, policy, programme, subsidy or support scheme. Causal chain The assumed path from “we do this thing” to “this outcome improves.” For example: extra support → better attendance → better grades. Causal inference Trying to work out whether the policy actually caused the change, rather than the change happening for some other reason. Quasi-experimental design A way of studying impact when you cannot do a full randomised trial, but still want a fair comparison. Econometrics Statistical methods often used in economics and policy to analyse real-world data. Regression discontinuity design Comparing people just either side of a cut-off point — for example, people just above and just below an eligibility threshold. Matching Finding a comparison group that looks as similar as possible to the group receiving the intervention. Difference-in-differences Comparing how things changed over time in one place that got the intervention with another similar place that did not. Robust data collection Collecting data carefully and consistently enough that you can trust the results. Pre and post comparison Looking at what things were like before and after a policy was introduced. Useful, but weak on its own because other things may also have changed. Comparison group A group or place that did not get the intervention, used to judge what might have happened otherwise. Units of randomisation The things being allocated to groups — people, schools, hospitals, cities, boroughs, etc. Phased rollout Introducing a policy gradually in different places or at different times, which can sometimes help researchers compare early and later areas. Observed characteristics Things you can measure about places or people, such as income, age, location, unemployment rates or population size. Municipality A local government area — roughly a city, town, borough or local authority. Food desert An area where people have poor access to affordable, healthy food. Support the show
