Some problems in identification of origins of lesser snow geese by chemical profiles

1979 ◽  
Vol 57 (12) ◽  
pp. 2292-2302 ◽  
Author(s):  
John P. Kelsall ◽  
Roland Burton
Keyword(s):  
Great Accuracy ◽  
Small Sample ◽  
Sample Sizes ◽  
X Ray ◽  
Chen Caerulescens ◽  
Snow Geese ◽  
Lesser Snow Geese ◽  
Chemical Profiles ◽  
Small Sample Sizes ◽  
Work Done

Results are reported of experimental work done to clarify problems and unknowns involved in identifying origins of lesser snow geese through computer-controlled, X-ray spectrometric analyses of primary flight feathers. Materials from wild and captive populations of lesser snow geese (Chen caerulescens caerulescens) were used. The use of an X-ray tube source of irradiation was successfully introduced to previously described techniques. Practical investigation of sample size for the generation of discriminant functions suggests that 30 samples per population are a barely adequate minimum and that 40 or more should be used.Given adequate sample sizes, and suitably different populations, the classification process overrides differences attributable to sex, age, and "Feather year" with great accuracy. With small sample sizes, however, those differences will dampen the accuracy of discrimination and classification, as will increasing numbers of populations. Examination of feathers from a captive flock, maintained for 4 years, shows statistically insignificant sexual differences, significant "within-year" changes in feather chemistry between October and May, and moderately consistent discrimination between year classes. Some of the chemical variables involved in the observed differences are examined.

Evidence for Toxoplasma gondii in migratory vs. nonmigratory herbivores in a terrestrial arctic ecosystem

2015 ◽  
Vol 93 (8) ◽  
pp. 671-675 ◽  
Author(s):  
S.A. Elmore ◽  
G. Samelius ◽  
C. Fernando ◽  
R.T. Alisauskas ◽  
E.J. Jenkins
Keyword(s):  
Toxoplasma Gondii ◽  
Small Sample Size ◽  
Small Sample ◽  
Arctic Ecosystems ◽  
Serum Samples ◽  
Arctic Ecosystem ◽  
Chen Caerulescens ◽  
Snow Geese ◽  
Lesser Snow Geese ◽  
Ross's Geese

It is currently unclear how Toxoplasma gondii (Nicolle and Manceaux, 1908) persists in arctic tundra ecosystems in the absence of felid definitive hosts. To investigate potential transmission routes of T. gondii in a terrestrial arctic food web, we collected samples from two migratory herbivores, Ross’s Geese (Chen rossi (Cassin, 1861)) and Lesser Snow Geese (Chen caerulescens (L., 1758)), and from two resident herbivores, Nearctic brown lemmings (Lemmus trimucronatus (Richardson, 1825)) and collared lemmings (Dicrostonyx groenlandicus (Traill, 1823)), trapped at Karrak Lake, Nunavut, Canada. Antibodies were detected in 76 of 234 (32.4%) serum samples from Ross’s Geese and 66 of 233 (28.3%) serum samples from Lesser Snow Geese. We did not detect T. gondii antibodies in filter-paper eluate tested from thoracic fluid samples collected from 84 lemmings. We did not detect T. gondii DNA in brain tissue from these lemmings. Although a small sample size, our findings suggest that lemmings in this terrestrial arctic ecosystem are not exposed to, or infected with, the parasite. This suggests that oocysts are not introduced into the terrestrial arctic ecosystem at Karrak Lake via freshwater runoff from temperate regions. This study demonstrated that live adult arctic-nesting geese are exposed to T. gondii and therefore migratory herbivorous hosts are potential sources of T. gondii infection for predators in terrestrial arctic ecosystems.

No Evidence that Experiencing Physical Warmth Promotes Interpersonal Warmth: Two Failures to Replicate Williams and Bargh (2008)

2018 ◽  
Author(s):  
Christopher Chabris ◽  
Patrick Ryan Heck ◽  
Jaclyn Mandart ◽  
Daniel Jacob Benjamin ◽  
Daniel J. Simons
Keyword(s):  
Null Hypothesis ◽  
Small Sample ◽  
Sample Sizes ◽  
Double Blind ◽  
Bayesian Analyses ◽  
Physical Warmth ◽  
Small Sample Sizes ◽  
Interpersonal Warmth

Williams and Bargh (2008) reported that holding a hot cup of coffee caused participants to judge a person’s personality as warmer, and that holding a therapeutic heat pad caused participants to choose rewards for other people rather than for themselves. These experiments featured large effects (r = .28 and .31), small sample sizes (41 and 53 participants), and barely statistically significant results. We attempted to replicate both experiments in field settings with more than triple the sample sizes (128 and 177) and double-blind procedures, but found near-zero effects (r = –.03 and .02). In both cases, Bayesian analyses suggest there is substantially more evidence for the null hypothesis of no effect than for the original physical warmth priming hypothesis.

scholarly journals G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Florent Le Borgne ◽  
Arthur Chatton ◽  
Maxime Léger ◽  
Rémi Lenain ◽  
Yohann Foucher
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Statistical Power ◽  
Small Sample ◽  
Causal Effects ◽  
Small Samples ◽  
Support Vector ◽  
Sample Sizes ◽  
Super Learner ◽  
Small Sample Sizes

AbstractIn clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power. Machine learning is also increasingly used because of its possible robustness to model misspecification. In this paper, we aimed to propose an approach that combines machine learning and G-computation when both the outcome and the exposure status are binary and is able to deal with small samples. We evaluated the performances of several methods, including penalized logistic regressions, a neural network, a support vector machine, boosted classification and regression trees, and a super learner through simulations. We proposed six different scenarios characterised by various sample sizes, numbers of covariates and relationships between covariates, exposure statuses, and outcomes. We have also illustrated the application of these methods, in which they were used to estimate the efficacy of barbiturates prescribed during the first 24 h of an episode of intracranial hypertension. In the context of GC, for estimating the individual outcome probabilities in two counterfactual worlds, we reported that the super learner tended to outperform the other approaches in terms of both bias and variance, especially for small sample sizes. The support vector machine performed well, but its mean bias was slightly higher than that of the super learner. In the investigated scenarios, G-computation associated with the super learner was a performant method for drawing causal inferences, even from small sample sizes.

scholarly journals What can we Learn from Studies Based on Small Sample Sizes? Comment on Regan, Lakhanpal, and Anguiano (2012)

2013 ◽  
Vol 113 (1) ◽  
pp. 221-224 ◽  
Author(s):  
David R. Johnson ◽  
Lauren K. Bachan
Keyword(s):  
Sample Size ◽  
Recent Article ◽  
Small Sample ◽  
Sample Sizes ◽  
Relationship Outcomes ◽  
Small Sample Sizes

In a recent article, Regan, Lakhanpal, and Anguiano (2012) highlighted the lack of evidence for different relationship outcomes between arranged and love-based marriages. Yet the sample size ( n = 58) used in the study is insufficient for making such inferences. This reply discusses and demonstrates how small sample sizes reduce the utility of this research.

Implications of Small Samples for Generalization: Adjustments and Rules of Thumb

2016 ◽  
Vol 41 (5) ◽  
pp. 472-505 ◽  
Author(s):  
Elizabeth Tipton ◽  
Kelly Hallberg ◽  
Larry V. Hedges ◽  
Wendy Chan
Keyword(s):  
Observational Studies ◽  
Small Sample ◽  
Average Treatment Effect ◽  
Small Samples ◽  
Sample Sizes ◽  
Random Samples ◽  
Rules Of Thumb ◽  
Large Populations ◽  
Small Sample Sizes ◽  
Combine Information

Background: Policy makers and researchers are frequently interested in understanding how effective a particular intervention may be for a specific population. One approach is to assess the degree of similarity between the sample in an experiment and the population. Another approach is to combine information from the experiment and the population to estimate the population average treatment effect (PATE). Method: Several methods for assessing the similarity between a sample and population currently exist as well as methods estimating the PATE. In this article, we investigate properties of six of these methods and statistics in the small sample sizes common in education research (i.e., 10–70 sites), evaluating the utility of rules of thumb developed from observational studies in the generalization case. Result: In small random samples, large differences between the sample and population can arise simply by chance and many of the statistics commonly used in generalization are a function of both sample size and the number of covariates being compared. The rules of thumb developed in observational studies (which are commonly applied in generalization) are much too conservative given the small sample sizes found in generalization. Conclusion: This article implies that sharp inferences to large populations from small experiments are difficult even with probability sampling. Features of random samples should be kept in mind when evaluating the extent to which results from experiments conducted on nonrandom samples might generalize.

scholarly journals Small sample sizes in high-throughput miRNA screens: A common pitfall for the identification of miRNA biomarkers

2018 ◽  
Vol 15 ◽  
pp. 1-5 ◽  
Author(s):  
M.G.M. Kok ◽  
M.W.J. de Ronde ◽  
P.D. Moerland ◽  
J.M. Ruijter ◽  
E.E. Creemers ◽  
...  
Keyword(s):  
High Throughput ◽  
Small Sample ◽  
Sample Sizes ◽  
Common Pitfall ◽  
Small Sample Sizes
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