1939-pearson.pdf: “"Student" as Statistician”, (1939-01-00; ):
[Egon Pearson describes Student, or Gosset, as a statistician: Student corresponded widely with young statisticians/mathematicians, encouraging them, and having an outsized influence not reflected in his publication. Student’s preferred statistical tools were remarkably simple, focused on correlations and standard deviations, but wielded effectively in the analysis and efficient design of experiments (particularly agricultural experiments), and he was an early decision-theorist, focused on practical problems connected to his Guinness Brewery job—which detachment from academia partially explains why he didn’t publish methods or results immediately or often. The need to handle small n of the brewery led to his work on small-sample approximations rather than, like Pearson et al in the Galton biometric tradition, relying on collecting large datasets and using asymptotic methods, and Student carried out one of the first Monte Carlo simulations.]
2008-ziliak.pdf: “Retrospectives Guinnessometrics: The Economic Foundation of “Student’s” t”, (2008-09; ):
In economics and other sciences, “statistical-significance” is by custom, habit, and education a necessary and sufficient condition for proving an empirical result (Ziliak and McCloskey, 2008; McCloskey & Ziliak, 1996). The canonical routine is to calculate what’s called a t-statistic and then to compare its estimated value against a theoretically expected value of it, which is found in “Student’s” t table. A result yielding a t-value greater than or equal to about 2.0 is said to be “statistically-significant at the 95 percent level.” Alternatively, a regression coefficient is said to be “statistically-significantly different from the null, p < 0.05.” Canonically speaking, if a coefficient clears the 95 percent hurdle, it warrants additional scientific attention. If not, not. The first presentation of “Student’s” test of statistical-significance came a century ago, in “The Probable Error of a Mean” (1908b), published by an anonymous “Student.” The author’s commercial employer required that his identity be shielded from competitors, but we have known for some decades that the article was written by William Sealy Gosset (1876–1937), whose entire career was spent at Guinness’s brewery in Dublin, where Gosset was a master brewer and experimental scientist (E. S. Pearson, 1937). Perhaps surprisingly, the ingenious “Student” did not give a hoot for a single finding of “statistical”-significance, even at the 95 percent level of statistical-significance as established by his own tables. Beginning in 1904, “Student”, who was a businessman besides a scientist, took an economic approach to the logic of uncertainty, arguing finally that statistical-significance is “nearly valueless” in itself.
1894-housman.pdf: “Robert Bakewell”, William Housman
1996-kadane.pdf: “Statistical Issues in the Analysis of Data Gathered in the New Designs”, Joseph B. Kadane, Teddy Seidenfeld
1931-fisher.pdf: “Pasteurised and Raw Milk”, R. A. Fisher, S. Bartlett
1933-elderton.pdf: “The Lanarkshire Milk Experiment”, Ethel M. Elderton
2015-polderman.pdf: “Meta-analysis of the heritability of human traits based on fifty years of twin studies”, (2015-05-18; ):
Despite a century of research on complex traits in humans, the relative importance and specific nature of the influences of genes and environment on human traits remain controversial. We report a meta-analysis of twin correlations and reported variance components for 17,804 traits from 2,748 publications including 14,558,903 partly dependent twin pairs, virtually all published twin studies of complex traits. Estimates of heritability cluster strongly within functional domains, and across all traits the reported heritability is 49%. For a majority (69%) of traits, the observed twin correlations are consistent with a simple and parsimonious model where twin resemblance is solely due to additive genetic variation. The data are inconsistent with substantial influences from shared environment or non-additive genetic variation. This study provides the most comprehensive analysis of the causes of individual differences in human traits thus far and will guide future gene-mapping efforts. All the results can be visualized using the MaTCH webtool.
One of the most important findings that has emerged from human behavioral genetics involves the environment rather than heredity, providing the best available evidence for the importance of environmental influences on personality, psychopathology, and cognition. The research also converges on the remarkable conclusion that these environmental influences make two children in the same family as different from one another as are pairs of children selected randomly from the population. The theme of the target article is that environmental differences between children in the same family (called “nonshared environment”) represent the major source of environmental variance for personality, psychopathology, and cognitive abilities. One example of the evidence that supports this conclusion involves correlations for pairs of adopted children reared in the same family from early in life. Because these children share family environment but not heredity, their correlation directly estimates the importance of shared family environment. For most psychological characteristics, correlations for adoptive “siblings” hover near zero, which implies that the relevant environmental influences are not shared by children in the same family. Although it has been thought that cognitive abilities represent an exception to this rule, recent data suggest that environmental that affects IQ is also of the nonshared variety after adolescence. The article has three goals: (1) To describe quantitative genetic methods and research that lead to the conclusion that nonshared environment is responsible for most environmental variation relevant to psychological development, (2) to discuss specific nonshared environmental influences that have been studied to date, and (3) to consider relationships between nonshared environmental influences and behavioral differences between children in the same family. The reason for presenting this article in BBS is to draw attention to the far-reaching implications of finding that psychologically relevant environmental influences make children in a family different from, not similar to, one another.
2012-morgan.pdf: “Rerandomization to improve covariate balance in experiments”, (2012-07-18; ):
Randomized experiments are the “gold standard” for estimating causal effects, yet often in practice, chance imbalances exist in covariate distributions between treatment groups. If covariate data are available before units are exposed to treatments, these chance imbalances can be mitigated by first checking covariate balance before the physical experiment takes place. Provided a precise definition of imbalance has been specified in advance, unbalanced randomizations can be discarded, followed by a rerandomization, and this process can continue until a randomization yielding balance according to the definition is achieved. By improving covariate balance, rerandomization provides more precise and trustworthy estimates of treatment effects.
[Keywords: randomization, treatment allocation, experimental design, clinical trial, causal effect, Mahalanobis distance, Hotelling’s T2]
2012-lee.pdf: “Correlation and Causation in the Study of Personality”, (2012-07-26; ):
Personality psychology aims to explain the causes and the consequences of variation in behavioural traits. Because of the observational nature of the pertinent data, this endeavour has provoked many controversies. In recent years, the computer scientist Judea Pearl has used a graphical approach to extend the innovations in causal inference developed by Ronald Fisher and Sewall Wright. Besides shedding much light on the philosophical notion of causality itself, this graphical framework now contains many powerful concepts of relevance to the controversies just mentioned. In this article, some of these concepts are applied to areas of personality research where questions of causation arise, including the analysis of observational data and the genetic sources of individual differences.
2015-rottensteiner.pdf: “Physical activity, fitness, glucose homeostasis, and brain morphology in twins”, (2015; ):
Purpose: The main aim of the present study (FITFATTWIN) was to investigate how physical activity level is associated with body composition, glucose homeostasis, and brain morphology in young adult male monozygotic twin pairs discordant for physical activity.
Methods: From a population-based twin cohort, we systematically selected 10 young adult male monozygotic twin pairs (age range, 32–36 yr) discordant for leisure time physical activity during the past 3 yr. On the basis of interviews, we calculated a mean sum index for leisure time and commuting activity during the past 3 yr (3-yr LTMET index expressed as MET-hours per day). We conducted extensive measurements on body composition (including fat percentage measured by dual-energy x-ray absorptiometry), glucose homeostasis including homeostatic model assessment index and insulin sensitivity index (Matsuda index, calculated from glucose and insulin values from an oral glucose tolerance test), and whole brain magnetic resonance imaging for regional volumetric analyses.
Results: According to pairwise analysis, the active twins had lower body fat percentage (p = 0.029) and homeostatic model assessment index (p = 0.031) and higher Matsuda index (p = 0.021) compared with their inactive co-twins. Striatal and prefrontal cortex (subgyral and inferior frontal gyrus) brain gray matter volumes were larger in the nondominant hemisphere in active twins compared with those in inactive co-twins, with a statistical threshold of p < 0.001.
Conclusions: Among healthy adult male twins in their mid-30s, a greater level of physical activity is associated with improved glucose homeostasis and modulation of striatum and prefrontal cortex gray matter volume, independent of genetic background. The findings may contribute to later reduced risk of type 2 diabetes and mobility limitations.
2012-vandongen.pdf: “The continuing value of twin studies in the omics era”, (2012-07-31; ):
The classical twin study has been a powerful heuristic in biomedical, psychiatric and behavioural research for decades. Twin registries worldwide have collected biological material and longitudinal phenotypic data on tens of thousands of twins, providing a valuable resource for studying complex phenotypes and their underlying biology. In this Review, we consider the continuing value of twin studies in the current era of molecular genetic studies. We conclude that classical twin methods combined with novel technologies represent a powerful approach towards identifying and understanding the molecular pathways that underlie complex traits.