Source: langll/Pixabay, used with permission
Media bias is perhaps most obvious when discussing concepts that polarize individuals. A contentious category of dichotomous beliefs is the legitimacy of climate change. One typically believes in the long-term consequences of escalating global temperatures, or one does not. Subjective inferences are escalated when research designs are inadequate or erroneously scrutinized, yet the study is published in a scientific journal or when lauded by writers who report research findings absent of methodological and statistical expertise. Misinformation perpetuates false beliefs among the public and is often cited as a cause of the “post-truth” era (Sinatra & Lombardi, 2019, p. 121), whereby individuals express views unsupported by confluent, replicable evidence but instead use personal experience or emotionally charged beliefs to substantiate their knowledge.
On November 20, 2020, The New York Times (NYT) published an article authored by journalist Veronica Penney with the ominous and anthropomorphic title “Climate Change Is Making Winter Ice More Dangerous.” The article interpreted the work of biologist Sapna Sharma and her colleagues (2020) who used correlation data to investigate the relationship among drowning deaths and air temperatures across ten different countries over variable time periods. According to the 16 researchers who conducted the original research study, ice related drownings are increasing due to escalating freeze/thaw cycles that accelerate the probability of weaker ice and thus enhance the frequency of fatal drownings. On face value this inference seems plausible, but under scrutiny the claim is indeed flawed and not justified by evidence.
Before outlining a series of methodological and interpretive concerns in the primary research study, it is important to understand why interpretation goes wrong, and the consequences of unjustified inferences. Chinn and Brewer (1993) in their seminal work on persuasion and conceptual change explained how individuals respond to contradictory scientific data that conflicts with personal understandings of the physical world. Their seven-step framework detailed reactions and responses to data considered anomalous, otherwise known as information or persuasive arguments that are inconsistent with one’s core beliefs, which result in enduring but erroneous perceptions. Within the framework, Chinn and Brewer explained that one approach to data inconsistent with beliefs is “reinterpretation” that entails intellectual pondering and potential endorsement, but after evaluation the person concludes that the new data is flawed, unclear, or irrelevant. When reinterpretation occurs existing conceptions remain intact, confirming prior beliefs and justifying familiar inferences that are often communicated to others.
Interpretation errors
Penney’s interpretive narrative is an example of communicating misinformation through apparent reinterpretation with the noble motivation of warning the general population of the real-life consequences of catastrophic climate change. If you play on the ice you may drown, and the presumed cause of the fatality is hot air. While Ms. Penney relies primarily upon direct quotations from the lead study author for her interpretations, Penny’s narrative quoted below perpetuates structural inaccuracies embedded in the original research:
1. “Drowning deaths are increasing exponentially.” This information is skewed by data from a limited number of higher incidence countries, while the authors intentionally excluded data from low incidence countries in the study analysis. Low sample sizes in some countries revealed that although “exponential” growth was described, actual death tolls were as low as 10 per year, including many year-to-year fatality declines.
2. “Some of the sharpest increases were in areas where Indigenous (sic) customs and livelihood require extended time on ice.” Implying disparate impact on certain minority populations, the narrative fails to indicate precise exposure time, which likely explains more death variability than temperature according to the study authors. Some indigenous populations spend substantially more time on the ice as part of their work and livelihood and thus the probability of drowning increases as exposure increases.
3. “The coronavirus pandemic could also put more people at risk.” No data in the original study addressed causes beyond temperature fluctuation. Penney justifies this COVID inference by a statement from the lead study author who asserted in Ontario “we have no place to go” thus more time will purportedly be spent outside.
The misrepresentations above likely originate from lack of interpretive knowledge or the proclivity to imply causality from correlation data. Considering the inability to control climate, unstandardized conditions across countries, variable reporting time periods, and dissimilar death categorizations by country, the methodology used in the original study is correlation at best. As such, NO causal generalizations are warranted. While the motivations of Ms. Penney are unknown, the narrative impact misleads the public and perpetuates the false inference that temperature, not foolish behavior increases drownings. Especially egregious is the reality that at least 10 other global media outlets including the Washington Post, CBC (Canada), and the BBC (United Kingdom) have also bastardized the primary study and use sensational headlines to make similar unjustified claims.
What the study actually indicates
I now explain the multiple methodological flaws that render the Sharma et al. (2020) study pragmatically useless.
1. The authors included 10 countries during data collection and arbitrarily excluded from the statistical analysis “Italy, Japan, and northern Canada from this analysis owning to the low number of drownings in these regions” (p.4). This biased exclusion is unjustified and has the probable impact of negating the statistical significance of the reported findings.
2. The research design used by the authors is a variation of the statistical method known as linear regression. The regression process entails examining numerous factors to determine how much change in the outcome (i.e. drownings) can be attributed to factors in the statistical model (i.e. country, temperature). Withstanding the egregious exclusion of low death countries from analysis, the authors revealed that 48% of the variance in drownings was accounted for by temperature. This means that an aggregate of other factors not measured in the current study (52%) contributed more to drowning deaths than temperature.
3. The statistical model used did NOT account for age, incidence of alcohol consumption, and type of vehicle if any involved (children, snowmobiles, and consumption are contributory to many ice drownings). The authors extracted information from Minnesota drownings to create histograms to reflect the potential impact of the excluded variables listed above however, arbitrarily exclude the data from statistical analysis without explanation.
4. The authors reported they were “unable to acquire data on non-fatal drownings,” (p. 4) leading to the conclusion that their inferences were a “conservative estimate” (p. 4) of the influence of temperature on drownings. However, the exclusion of non-fatal drownings means the proportion of those on the ice who have drowned may have decreased, if the ratio of deaths to total incidents has declined. Absent of knowing the precise frequency of ice activities the inference of drownings increasing exponentially is decontextualized. Conventional wisdom would suggest that more time on the ice equals more drowning potential, yet the frequency on ice variable is unaccounted for in any of the analyses.
5. Country data covered different years and time periods. The aggregation of different time-period data means that prevailing conditions across countries are assumed to be similar. In reality, year-to-year conditions change suggesting handpicking the data under the rationalization that comparable statistics were unavailable across countries.
This flawed research does NOT mean climate change is fake
In summary, although the evidence supporting climate change in most other studies is standardized and replicable, the embellishment in this case is unjustified by the data or the analysis. The authors and their media minions have succumbed to the most common thinking flaw of confirmation bias, whereby the individual selectively chooses which data, in which form, and in which frequency to rationalize their pre-existing beliefs. It is unfortunate that the public may misconstrue Sharma’s findings as accurate and generalizable, when under scrutiny the concerns described here crystalize how misinformation is often based on flawed premises and in this case some very thin ice.
