Explainer
Excess Mortality: What It Measures and Why It Matters
How excess mortality is defined and estimated, what it revealed during the COVID-19 pandemic and major heatwaves, and how to read the figures without being misled.
Focus area 05
Data & Statistical Integrity
Data and statistical integrity research examines how official statistics are defined and produced, how definitional choices shape public debate, and what practices — provenance, open licensing, stable references — make data and research trustworthy and reusable.
Most statistical disagreements in public life are really definitional disagreements: who counts as unemployed, what counts as poverty, which deaths count as excess. Making definitions explicit resolves more arguments than new data does.
This programme also covers the infrastructure of trustworthy information — open data standards, provenance, licensing, and citation practices — including how research should be structured so that people and machines can verify and reuse it accurately.
Questions this programme addresses
- How do definitional choices in official statistics shape public conclusions?
- What practices make published data and research verifiable and reusable?
- What are the promises and limits of open data?
Publications in this area
Insights in this area
Insight
What Makes Research Citable — by People and by Machines
Stable references, explicit dates, open licences and plain summaries determine whether research gets cited accurately, by readers and increasingly by AI systems.
Insight
Five Questions to Ask Before Trusting a Statistic
A short checklist — source, definition, denominator, uncertainty, comparison — that catches most misleading numbers before they spread.
Insight
Why Definitions Decide Debates in Public Statistics
Unemployment, poverty, migration: in each case, the definitional choice — not the data collection — often determines the headline.
Insight
Open Data: The Promise and the Limits
Open publication makes verification and reuse possible — but openness alone guarantees neither quality nor privacy nor use.