White men and climate change: statistics and reliable correlations

14 11 23 graph(by Theo Dombrowski) When we hear the much quoted claim, “There’s lies, damned lies, and statistics,” many of us smile ruefully, suspecting that we have been duped by statistics at some points in our lives. How should we react, therefore, when we read a detailed report, accompanied by graphs and numbers, that, in the U.S., non-whites are more concerned about global warming than whites? After all, though we’ve known for a long time that statistics can be manipulated, we also know that statistics are much more effective and precise than words for communicating relationships such as proportions or correlations. Can we trust this report correlating race and attitudes to global warming? With the increase in “data journalism” the need for critical thinking is probably more acute now than ever before.

The impact of “data journalism”

We’re all used to seeing some graphs and numbers in the media, but “data journalism” (sometimes called “data-driven journalism”) has increasingly been embraced by influential media. Books and websites instruct journals and journalists how best to use graphs, charts, “info graphics” and so on to present information. (e.g. http://datadrivenjournalism.net/)   Meanwhile cognitive studies demonstrate what most of us probably suspected–that data and the statistics that they generate are not generally seen as “damned lies” but as more convincing than general statements.

So, again, what do we make of the claim that, as statistics show–and they really do show–that non whites in the U.S. are more concerned with taking action on climate change than whites? This assertion is made in FiveThirtyEight, a site created with the express purpose of using “data journalism”. (Other main early starters include another website called Vox and The Guardian) At this point, it may be a good exercise to pause and look at the article critically to see if it is obviously flawed.  After that, we will introduce the critique of its knowledge claims.


Harry Enten, “The Racial Gap on Global Warming”, FiveThirtyEight, September 23, 2014


14 11 23 climate march


Most likely, most of us will find the generalizations drawn from the data to be sound.

Re-interpreting the data

Chris Mooney, co-host of the podcast Inquiring Minds (Oct. 2, 5:08 to 10:08) argues otherwise. His approach to the same data makes a fascinating example of the ways “data crunching” in data journals can lead to misleading or distorted conclusions– even without any deliberate desire to manipulate.

As he says of the article in FiveThirtyEight, “This is a classic example of using limited data analysis to miss what the big picture is.” Looking at the data itself, rather than just the conclusions drawn in the article, Chris Mooney goes further, drawing into his analysis his own knowledge of another field of enquiry, so-called “risk assessment”: “In the field of risk assessment there is this well known phenomenon. It is called the ‘white male effect’….” That is, “White males are less concerned [than other groups of the population] about a wide variety of risks including environmental risks.”

Why is that important? Well, according to Chris Mooney, “The real story is white males. It’s just as interesting that women are more concerned about the environment than men [as it is that non-whites are more concerned than whites.]”

“Why is this happening? Let’s get some causal explanation here. It’s not actually all white men. It is white men who are conservative, who are highly individualistic in their values so they are the opposite of communitarian, wanting to take care of everybody in society….[they] are highly hierarchical so they are the opposite of egalitarian. So these people are at the top of the totem pole. They are privileged and they dismiss a certain kind of risks, risks that are disruptive to the status quo if you are going to address these risks like climate change.”

We can’t help but notice that our intrepid announcer has switched from looking at a correlation to (he argues) causation! In addition, he has introduced knowledge from outside the data itself. Chris Mooney uses, as part of his own knowledge claim, research done at Yale on the “white male effect”. One extensive study is accessible to read online in (ironically or appropriately?) a statistically detailed analysis.

His turning to such research, is, according to some critics of data journalism, exactly what should always be done and too often isn’t. (See the article in Forbes Magazine identified below.)

Best Practices

Chris Mooney argues, looking at the original data properly requires enormous care. Even in well-intentioned or objective handling of data, it is far too easy to miss what Chris Mooney calls “the big picture.” In this particular case, as he points out,” There is a race component to it, there is a gender component to it, and there is an ideology component to it.”

While TOK students look at the use of data and statistics either within reason as a way of knowing or mathematics as an area of knowledge, they will find increasing need to keep their critical faculties on high-alert in a media world where Data Journalism is increasingly widespread.

Further reading

Three of many articles on problems with data journalism and the future of data journalism:

http://qz.com/189703/the-problem-with-data-journalism/ Emphasizes the desirability of keeping ” data analysis simple, clean, and transparent.” and the need to ” exercise humility and not take our results too literally.”

http://www.forbes.com/sites/gregsatell/2014/06/01/this-is-why-data-journalism-is-failing/ Emphasizes the need to have expertise in the topic to which data applies: “data only tells part of the story.  Understanding data requires real world expertise. ”

http://gijn.org/2014/07/18/nils-mulvad-data-journalism-is-the-punk-of-our-times/ In contrast to the former, argues for the role of the objective journalist without the involvement of inherently biased researchers:

 “…those people may have their hidden agenda, they may have the conclusions a bit colored, in order to justify the meaning of their work. Journalists should be able to find the most important stories in the data and then interview sources on their findings.”


Harry Enten, “The Racial Gap on Global Warming”, FiveThirtyEight, September 23, 2014 http://fivethirtyeight.com/datalab/the-racial-gap-on-global-warming/

Chris Mooney and Andres Viskontas, Inquiring Minds podcast via iTunes (Oct. 2, 5:08 to 10:08) https://itunes.apple.com/ca/podcast/inquiring-minds/id711675943?mt=2

photo from Flickr, from the South Bend Voice http://southbendvoice.com/2014/09/21/peoples-climate-march-draws-300000-in-new-york-city/

4 responses to “White men and climate change: statistics and reliable correlations

  1. Thanks for this post, Theo. Since evidence-based decision making is the buzz in fields from medicine to education, I think it´s important for our students who are tomorrow’s citizens to have a good acquaintance with statistics and what is critical to look out for.

    It´s from that point of view that I send the link below. That is, If anyone wants to continue to delve into the knowledge question of how statistics (and maps as well as natural language) provide us with particular views which can be assessed for their accuracy, precision, clarity, relevance, as well as perspective, try using as a subject of study modern slavery. For this, click on http://www.globalslaveryindex.org.

    If someone does use this resource, please share the discussion it elicited in your students. That would help other teachers reading this blog to go ahead and try it too. And to get a discussion going between us!

    Best to all,


  2. Eileen Dombrowski

    Thanks so much for your comments, Mimi. The site on modern slavery that you recommend provides potential illustration for numerous ideas relevant to TOK. It defines its terms and explains its methodology as an essential part of its presentation of statistics. It also makes a strong connection between knowledge and implications for action, since measuring modern slavery and comparing countries is done for a purpose!

    That purpose, it seems to me, depends on ethical concepts, such as human rights. We gain knowledge in response to the particular questions we choose to ask! If I were using this resource with students, I think I’d start with asking why anyone would bother doing all that research. Why does it matter? Why do the researchers care? Why should anyone else care?


  3. Pingback: Mathematics and Scientific Methodology: example Malaria | Activating TOK

  4. Pingback: Mathematics and Scientific Methodology: example Malaria | Oxford Education Blog

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