Wood Romances

In previous posts in this series , I've tried summarize what I consider bad behavior among those debating climate change. So far I've discussing what I consider statistically unethical practices - using short-term trends, misusing scales, and using local instead of global data. Here I'd like to look how we make comparisons between types of data. In climate science, we need to deal with all sorts of different kinds of data. For global temperatures, I can think of at least 3 - proxies, the instrumental record (satellites, thermometers), and models (reanalysis, modeled projections, etc). It's perfectly fine to create graphs that include all of these kinds of data, but in doing so, we have to make sure that we are communicating honestly and making comparisons accurately. Proxy data has lower resolution and larger confidence intervals than the instrumental record, and modeled projections always depend in part on the assumed scenario, and confidence intervals for these projec...

This is Wrong Whenever you read the scientific literature, you'll frequently encounter estimates that include "confidence intervals." Generally speaking most of us understand these to give us an understanding of how sure scientists are that in their estimates they "got it right." And yet there is frequently confusion and even miscommunication about what confidence intervals mean that can sometimes unintentionally cause us to misrepresent (even if slightly) what scientists are actually claiming. It's something I've done in the past, and it's something that affects many well-intentioned people just trying to represent the data accurately. What I want to do in this post is to accurately describe what is being stated when confidence intervals (CIs) are reported, and then offer some reflections on the way this can impact our communication of the confidence we can have in scientific evidence. Confidence vs Probability At the heart of the confusion is a fai...