Primer on Natural Resource Science

Index

Index Acknowledgments section, 160 active voice, 162–63 additive vs. compensatory mortality , 42–43, 73–74 Akaike information criterion (AIC), 113–24; description of, 113–15, 123–24; interpretation of analyses, 117; logistic regression example, 118–19; model development and, 115–17; polynomial regression example, 119–22; uses of, 122–23 algorithms, as releaser of creativity , 59 alternative hypotheses: in null hypothesis significance testing, 103, 107; testing of, with model selection, 118–19 ambiguity: of facts, 41; as releaser of creativity, 60 ambivalence of facts, 42–43 analysis of variance (ANOVA), 82, 150 analytical methods, list of, 111 anomalies, as vehicle for creativity , 57 ANOVA (analysis of variance), 82, 150 arbitrary constructs, formulation of, 94 articles. See journal articles assumptions of science, 3–7 backdating bias, 92–93 beliefs, 37, 45 bias: Akaike information criterion and, 113–14; backdating bias, 92–93; confirmation bias, 48; detection of personal, 76; empirical bias, of resource science, 77; in journal articles, 75; of journals, 158–59 bounded possibility, 50 brain. See mind brevity in writing, 162 bubonic plague, 60 case studies, 150 causation: confirmation bias and, 48; correlation compared to, 21; determination of, 20–21, 82–83; multiple, 30–31 causes, chain-of-events, observational studies and, 86 central limit theorem, 101–102 chain-of-events causality, observational studies and, 86 challenge to existing knowledge: as countermeasure to human limitations, 53; in critical thinking, 70–71; as releaser of creativity, 56–57, 60 clarity in writing, 162 class 1 (existential) facts, 37, 45 class II facts, 37–44, 45 classical experimental design, statistical inference and, 149–50 classification methods, new, as creative act, 56 compensatory vs. additive mortality , 42–43, 73–74 Competitive Exclusion Principle, 10, 16–17 complex field processes, mathematical methods and, 97–98 comprehensibility of writing, 163 compression of morbidity hypothesis, 27–28 computer-assisted logistic regression analysis, 141 184 Index conceptual benefits of mathematics , 93–94 confirmation bias, 48 conjectural hypotheses, 19 consensus, facts as phenomena of, 36–37, 38 conspecifics, allegiance to, 50–52 construct. See Poincaré hypotheses contingency of facts, 38 controls: in experimental science, 82, 86; in observational science, 83–84, 86 correlation, 21 correlation of variables, in modeling , 140–41 Cp criterion, 120–21 creativity, 55–64; epiphanies and, 63; examples of, 56–57; inhibitors of, 58–59; releasers of, 59–63, 64; science and, ix, 55–56, 64 critical thinking, 65–77; active practices, 74–76; in life, 76; looking at limits, 71–74; need for in science, 65, 77; thought experiments and, 66–71, 77 culture of science, 50, 53, 169–70 data, statistics in abstraction of information from, 101–102 deduction: as creative act, 57; definition of, 4, 25; examples of, 25–29; flaws in application of, 34–35; interdependence with induction, 169; philosophical considerations, 29–32, 151 description, 7–8 descriptive statistics, 101–102 descriptive studies: hypotheses and, 15–16, 22; model selection and, 122, 123; role of, 7–8; value of, 153. See also observational science development and testing of methods and techniques, 12, 152 dichotomous situations, null hypothesis significance testing and, 108 Discussion section, 160 double-blind studies, 53 dysphemistic wording, bias and, 75 ecological interpretation of research studies, 149 ecology, perspective on statistics, 110–12 ecosystems, complexity of, and Ockham’s Razor, 7 effective strip width, estimation of, 91–92 ego, creativity and, 58, 60 Einstein, Albert, 66 Elements of Style (Strunk and White), 167 empirical bias, 77 empiricism, 13 ends (growing knowledge) vs. means (methods), x, 148–50, 155 epiphanies, 63 equations: creativity and, 62; as symbolic representations, 95–98 errors, type I and type II, 103–104 euphemistic wording, bias and, 75 evanescence of Class II facts, 43–44 exemplars, 49–50 existential (Class I) facts, 37, 45 existential hypotheses, 18–19 experimental controls. See controls experimental design, classical, statistical inference and, 149–50 experimental methodology, as countermeasure to human limitations, 53 experimental science: belief in superiority of, 87; description 185 Index of, 82–83, 88; examples of, 81, 82–83; mathematical models in, 84–86; natural resource science and, 169; observational science compared to, 86–88, 89 exponential growth models, 130 exponentiation, 128–29 extradisciplinary knowledge, 60–62 extraneous variables: control of, in experimental studies, 83, 86, 88; observational study design and, 83–84, 86; parameterization of, 86 extremes, examination of, 71–74, 93–94 facts, 36–45; ambiguity of, 41; ambivalence of, 42...