Advice for Policy Makers and Researchers

While this was written to assist government scientists and policy makers better understand each other these are both very good lists, compiled by British and Australia policy makers and researchers. They capture the dynamic well between the technical expert and the more general policy advisor roles and perspectives, and tap into themes of ideology, evidence and risk of Policy Arrogance or Innocent Bias: Resetting Citizenship and Multiculturalism.

Good reading both within the public service and with the political level, given some of the ongoing tensions regarding evidence and anecdote and how the different perspectives play out.

Top 20 things scientists need to know about policy-making

1. Making policy is really difficult
2. No policy will ever be perfect
3. Policy makers can be expert too
4. Policy makers are not a homogenous group
5. Policy makers are people too
6. Policy decisions are subject to extensive scrutiny
7. Starting policies from scratch is very rarely an option
8. There is more to policy than scientific evidence
9. Public opinion matters
10. Economics and law are top dogs in policy advice
11. Policy makers do understand uncertainty
12. Parliament and government are different
13. Policy and politics are not the same thing
14. The UK has a brilliant science advisory system
15. Policy and science operate on different timescales
16. There is no such thing as a policy cycle
17. The art of making policy is a developing science
18. ‘Science policy’ isn’t a thing
19. Policy makers aren’t interested in science per se
20. We need more research’ is the wrong answer

Top 20 things politicians need to know about science

1. Differences and chance cause variation
2. No measurement is exact
3. Bias is rife
4. Bigger is usually better for sample size
5. Correlation does not imply causation
6. Regression to the mean can mislead
7. Extrapolating beyond the data is risky
8. Beware the base-rate fallacy
9. Controls are important
10. Randomisation avoids bias
11. Seek replication, not pseudoreplication
12. Scientists are human
13. Significance is significant
14. Separate no effect from non-significance
15. Effect size matters
16. Data can be dredged or cherry picked
17. Extreme measurements may mislead
18. Study relevance limits generalisations
19. Feelings influence risk perception
20. Dependencies change the risks