In the run up to the ICM 2026, we speak to one of the invited speakers at the Congress, Richard Samworth, all about his work in statistics.
Causal inference is the art of discerning cause and effect from data. Find out more in this introduction.
RCTs are the gold standard when it comes to testing whether an intervention, such as a new medical drug, works.
Data can give us incredibly useful insight, but they can also mislead. Here's an example.
How do you discern cause and effect when you can't do a controlled experiment? Directed acyclic graphs (DAGs) are a fun and important tool.
Cognitive biases shape how we understand data. Being aware of them gives us a better chance of avoiding bias.
What does it mean to say there's a 30% chance of rain today?
Does knowing statistics about a whole population tell you how they apply to you?
The way we talk about numbers affects the decisions people make - so think carefully about what you say!
How shocking is it when your risk of getting a particular disease doubles?
Weighing up risks is something we have to do every day and sometimes the stakes are high. Find out what to keep in mind when doing so!
How can statistics help us to make informed decisions. Find out with this brief explanation of hypothesis testing.
