Healthy debate in science: A reply to Martín-Olalla

Abstract

In a letter to Time & Society, José Maria Martín-Olalla (2023b) offers a critique of my team's study on traffic fatalities in misaligned time zones in the United States (Gentry et al., 2022). Unlike research that is met only with the sound of crickets, it is positively refreshing when other scholars take notice. This essay is reminiscent of his response to Malow (2022) on the subject of Daylight Saving Time (Martín-Olalla 2023a), and other constructive critiques.Our study identified a 21.8% elevated risk of traffic fatalities in eccentric time localities (ETLs) compared to solar zones in the United States. Dr. Martín-Olalla's essay takes issue with what he calls this "staggering large" result (p. 1). His subtitle is "Not that elevated" (p. 1).In framing my reply it helps to consider Martín-Olalla's (2023b) early statement, "In their analysis, the authors implicitly assume that F [fatalities] scales with P [population] through different geographic localities" (p. 1). Far from assuming, we tested a falsifiable hypothesis using data from the Fatality Analysis Reporting System (FARS) annual census. This carefully managed resource, curated by the National Highway Traffic Safety Administration (NHTSA), enumerates each U.S. highway death by county. Whole-population data carry the advantage of no standard error and no need to eliminate data (Alexander, 2015), a concept we explained rather than assumed. Whole-population data are appropriately analyzed via descriptive rather than inferential statistics.In contrast, Dr. Martín-Olalla's (2023b) extensive meta-statistical analyses seem to assume we collected only a sample of the population rather than the whole population. Note his concern about our "scarcity of observations" (p. 3). In fact, we accounted for each road fatality over a 12-year period, not a mere sample (n = 417,399). We cited Alexander (2015) and Wood (2016), who explain why inferential-statistical tests are not only unnecessary for whole-population data, but counterproductive. Wood (2016) notes, "Obviously if you've got the time and energy and resources to study the whole population you should do so because then you get the whole answer and don't need to mess around with p values or