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Logarithmic scales in ecological data presentation may cause misinterpretation. | LitMetric

AI Article Synopsis

  • Scientific communication heavily relies on the effective presentation of data, particularly through the use of logarithmic scales, which are common in fields like ecology.
  • An analysis revealed that in 2015, 22% of papers in the journal Ecology had log-scaled axes, with 21% displaying log-log scales. A survey showed much lower correct interpretations for log-log graphs (56%) compared to linear-linear graphs (93%).
  • The findings indicate widespread misunderstandings of log-scaled data; thus, it is recommended that ecology education includes specific training on these scales and that authors consider potential reader misconceptions when visualizing data.

Article Abstract

Scientific communication relies on clear presentation of data. Logarithmic scales are used frequently for data presentation in many scientific disciplines, including ecology, but the degree to which they are correctly interpreted by readers is unclear. Analysing the extent of log scales in the literature, we show that 22% of papers published in the journal Ecology in 2015 included at least one log-scaled axis, of which 21% were log-log displays. We conducted a survey that asked members of the Ecological Society of America (988 responses, and 623 completed surveys) to interpret graphs that were randomly displayed with linear-linear or log-log axes. Many more respondents interpreted graphs correctly when the graphs had linear-linear axes than when they had log-log axes: 93% versus 56% for our all-around metric, although some of the individual item comparisons were even more skewed (for example, 86% versus 9% and 88% versus 12%). These results suggest that misconceptions about log-scaled data are rampant. We recommend that ecology curricula include explicit instruction on how to interpret log-scaled axes and equations, and we also recommend that authors take the potential for misconceptions into account when deciding how to visualize data.

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Source
http://dx.doi.org/10.1038/s41559-018-0610-7DOI Listing

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