Writing for publication entails many drafts and many reviews of these drafts by friends, colleagues, copyeditors, and critics. The credibility of science depends on peer review, as explained by Frederick Grinnell in The Everyday Practice of Science and by me in Chapter 9 of Tools for Critical Thinking in Biology, where I give personal examples, historical examples, and contemporary examples that have been prominent in the news media.
Two readers of the published version of my book made several suggestions for corrections. I wish to thank them here and explain what they found in their careful reading. Don’t get bogged down in the first explanation dealing with a problem that appears earliest in the book, but return to it after reading some of the later explanations that are more straightforward.
1) In Chapter 5, I use two main examples to show how researchers use, and sometimes abuse, comparative and correlational data to answer questions about causation. For instance, people have wondered whether microwave radiation emitted by cell phones can cause brain cancer. One source of evidence to address this question comes from comparison of past cell phone use by people with and without brain cancer; this is called a retrospective case-control study. Cases are people with brain cancer, controls are people without brain cancer, and the study is retrospective because the data come from estimates by cases and controls of how much they used cell phones in the past. Case-control studies often use odds ratios to report results: what are the odds of brain cancer for a cell phone user compared to those for a nonuser? More specifically, what is the ratio of these odds? If this ratio is greater than 1, a cell phone user is more likely to get brain cancer than a nonuser, although we also have to consider the possibility that a ratio greater than 1 could simply be due to chance.
Odds ratios are a little tricky to understand, so I use a completely different example to show how they are calculated. When the Titanic sank in 1912, 650 adult males died and 132 survived, while only 102 adult females died and 300 survived. Therefore the odds of dying for males were 650/132 = 4.92 while the odds of dying for females were 102/300 = 0.34. These translate to an odds ratio of 4.92/0.34 = 14.5. I then wrote “adult male passengers were almost 15 times as likely to die when the Titanic sank as adult female passengers.” As one post-publication reviewer noted, this is incorrect; the odds ratio really means that the odds of dying were 15 times greater for males than females. We can also express the difference in mortality of male and female passengers on the Titanic as a relative risk. The likelihood of death for males was 650/(650+132) = 0.83; the likelihood of death for females was 102/(102+300) = 0.25. Therefore males were 3.32 times as likely to die as females (relative risk = 0.83/0.25). This is a pretty dramatic difference, but not as dramatic as I implied in the book.
Using odds to express probabilities of events has a long history that is intertwined with the history of gambling. According to FanGraphs on March 8, 2016, the Chicago Cubs have an 18.6% chance of winning the 2016 World Series. This means that the odds against the Cubs winning are 81.4%/18.6% = 4.38 to 1. If you bet $1 on the Cubs today, your friend should be willing to pay you $4.38 if they win in exchange for collecting $1 from you if they lose. Although odds and odds ratios are somewhat indirect measures of relative probabilities, they have some advantages for statistical analysis of medical questions like the relationship of cell phone use to brain cancer.
2) I describe how interactions between genes and environments influence human traits in Chapter 7, using studies of twins to develop this theme. I introduce this topic with a story about two genetically identical twins that were adopted at an early age and raised by separate families. These twins were reunited at age 39 and discovered that they shared a remarkable set of traits, starting with the fact that both of their adoptive families named them Jim. In addition, as I wrote, “both Jims smoked the same brands of cigarettes and beer – amazing!” As one post-publication reviewer noted, smoking beer is pretty amazing.
3) In Chapter 8, I used Hurricane Katrina to introduce the idea that events often result from complex webs of causation. About 1800 deaths and $81 billion in property damage in New Orleans were attributed to Katrina, which hit the Gulf Coast on August 29, 2005, but several other factors also contributed to this devastation. My diagram of this web of causation implies that George W. Bush’s praise of his director of the Federal Emergency Management Agency (“Brownie, you’re doing a heckuva job”) was one of these contributing factors. Instead, I should have described this praise as a symptom of an underlying cause, appointments of unqualified people to lead agencies such as FEMA.
4) Jim Estes of the University of California, Santa Cruz and the US Geological Survey has studied sea otters and other marine mammals in the Aleutian Islands for more than 40 years. Chapter 8 describes some of their research, including their discovery that predation by killer whales caused the population of sea otters to crash in the 1990s. This is a fascinating, multidimensional story involving questions such as these: The researchers first saw an attack by a killer whale on a sea otter in 1991 – why did killer whales switch from larger mammalian prey to sea otters at this time? The researchers saw six total attacks in the early 1990s, yet estimated that the sea otter population declined from 53,000 in 1991 to 12,000 in 1997 – could predation by killer whales account for such a large decline without more attacks being seen by humans? Sea otters are much smaller than other mammalian prey of killer whales – how many sea otters would supply the daily energy needs of a killer whale? I address this last question in Box 5.2. Killer whales require about 247,500 kilocalories of energy per day, and five sea otters would supply this amount of energy. One kilocalorie equals 1 Calorie in human nutrition (not 1000 Calories, as I state in the book). We use 2,000 to 3,000 Calories per day, only about 1% of the energy a killer whale uses. Of course, we’re much closer in size to a sea otter than a killer whale.
5. Chapter 8 features several predators – sea otters that eat sea urchins, killer whales that eat sea otters, and wolves that eat elk. It describes the big effects that these predators can have on their prey and on the habitats where both live. I emphasize different kinds of evidence that researchers have gathered to understand these relationships between predators and prey in ecological communities, but I also ask readers to consider how humans influence these communities. These aren’t purely scientific issues, but entail decisions about management – commercial fishing in the Aleutians, hunting in the Rockies, removal of wolves that kill livestock near Yellowstone National Park. These decisions involve not only science but ethics. In closing Chapter 8, I quote A Sand County Almanac by the conservationist Aldo Leopold: “I was young then, and full of trigger itch; I thought that because fewer wolves meant more deer, that no wolves would mean hunter’s paradise. But after seeing the green fire die, I sensed that neither the wolf nor the mountain agreed with such a view.” As one of my post-publication reviewers reminded me, the “green fire” refers to the wolf’s eyes.
6. I use a rubric called FiLCHeRS to summarize six key tools for critical thinking: evaluation of claims by assessing their Falsifiability, Logic, Comprehensiveness, Honesty, Replicability, and Sufficiency. James Lett proposed this rubric in 1990 and illustrated its use in evaluating paranormal beliefs such as astrology and extrasensory perception. I apply FiLCHeRS to claims by those who dispute the scientific evidence for human impacts on climate change. In discussing sufficiency, I quote Lett: “extraordinary claims demand extraordinary evidence.” This idea actually has a long history, starting with the philosopher David Hume in 1748, continuing with a restatement by Laplace in 1812, another restatement by Carl Sagan in 1980, and finally Lett’s version in 1990 that changed Sagan’s verb from “require” to “demand”.
7. Finally, I use the classic case of industrial melanism to explain how evolution works in Appendix 2. Peppered moths fly at night and rest on tree trunks during the day. All peppered moths collected in England before 1948 were light-colored, providing effective camouflage against predation by birds when resting on patches of whitish lichens growing on tree trunks. People began to collect dark-colored moths in the mid-1800s, and virtually all moths collected in industrial areas were dark by 1898. By this time, soot from factories had killed the lichens and coated the tree trunks, so dark moths were better camouflaged, as illustrated in Figure A2.1. In short, pollution had two effects that changed the environment for peppered moths – it killed lichens on tree trunks, exposing the naturally darker color of the trunks, and the particulate components of the pollution (soot) made the trunks even darker. With pollution controls starting in England in the 1950s, soot on trees was gradually washed away by rain, lichens returned to the trees, and light-colored moths were again favored by natural selection. H. B. D. Kettlewell did several experiments in polluted and unpolluted woods to test the hypothesis that predation by birds was the agent of selection on moth populations, with light moths being better camouflaged in unpolluted woods and dark moths being better camouflaged in polluted woods.