DO ENVIRONMENTAL FACTORS AFFECT THE TAXONOMIC RELIABILITY OF LEAF CUTICULAR MICROMORPHOLOGICAL CHARACTERS? A CASE STUDY IN PODOCARPACEAE

2017 ◽  
Vol 74 (3) ◽  
pp. 299-343 ◽  
Author(s):  
J. A. R. Clugston ◽  
C. E. Jeffree ◽  
A. Ahrends ◽  
R. R. Mill
Keyword(s):  
Environmental Factors ◽  
First Record ◽  
Small Sample ◽  
Climate Data ◽  
Sample Sizes ◽  
Small Sample Sizes ◽  
Micromorphological Characters ◽  
Multiple Samples ◽  
The Relationship

Leaf cuticle micromorphology has been cited as an important set of taxonomic characters in gymnosperms, but previous studies have largely been based on small sample sizes. The premise of this study was to understand whether external factors affect cuticular micromorphology of Podocarpaceae. Two example species, Prumnopitys andina and Podocarpus salignus, were studied. Of 21 sampled characters, nine (c.43% of the total) were visually assessed as being moderately reliable or highly reliable for taxonomic discrimination for both species, with an additional six (c.29%) being moderately reliable or highly reliable for only one or other of the example species, and six characters (c.29%) unreliable for both. Seven of the most variable stomatal characters were selected for further analysis to establish whether environmental factors affect them. The relationship between these seven stomatal characters, the environment and climate was analysed using the R ‘vegan’ package and climate data gathered from WorldClim. Our results showed that both species had larger stomata in moist and shady conditions, and a higher density of (smaller) stomata in sunny and drier conditions. An additional novel finding was the presence of stomata on the adaxial leaf surface in 46% of samples of Prumnopitys andina: the first record of adaxial stomata in this species, highlighting the necessity of studying multiple samples of a given species. In conclusion, these results indicate that larger sample sizes than have hitherto been employed in cuticle micromorphological studies are necessary to fully document the amount of phenotypic variation that exists.

The Relationship Between Sample Sizes and Effect Sizes in Systematic Reviews in Education

2009 ◽  
Vol 31 (4) ◽  
pp. 500-506 ◽  
Author(s):  
Robert Slavin ◽  
Dewi Smith
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
Secondary Mathematics ◽  
Significant Negative Correlation ◽  
Small Sample ◽  
Effect Sizes ◽  
Sample Sizes ◽  
Large Samples ◽  
Small Sample Sizes ◽  
The Relationship

Research in fields other than education has found that studies with small sample sizes tend to have larger effect sizes than those with large samples. This article examines the relationship between sample size and effect size in education. It analyzes data from 185 studies of elementary and secondary mathematics programs that met the standards of the Best Evidence Encyclopedia. As predicted, there was a significant negative correlation between sample size and effect size. The differences in effect sizes between small and large experiments were much greater than those between randomized and matched experiments. Explanations for the effects of sample size on effect size are discussed.

scholarly journals What’s in a Coefficient? The “Not so Simple” Interpretation of R2, for Relatively Small Sample Sizes

2019 ◽  
Vol 7 (12) ◽  
pp. 27
Author(s):  
Nizar Zaarour ◽  
Emanuel Melachrinoudis
Keyword(s):  
Standard Deviation ◽  
Linear Regression ◽  
Small Sample ◽  
Coefficient Of Determination ◽  
Simple Linear Regression ◽  
Sample Sizes ◽  
Simple Interpretation ◽  
Small Sample Sizes ◽  
Level Of Significance ◽  
The Relationship

There are several misconceptions when interpreting the values of the coefficient of determination, R2, in simple linear regression. R2 is heavily dependent on sample size n and the type of data being analyzed but becomes insignificant when working with very large sample sizes. In this paper, we comment on these observations and develop a relationship between R2, n, and the level of significance α, for relatively small sample sizes. In addition, this paper provides a simplified version of the relationship between R2 and n, by comparing the standard deviation of the dependent variable, Sy, to the standard error of the estimate, Se. This relationship will serve as a safe lower bound to the values of R2. Computational experiments are performed to confirm the results from both models. Even though the focus of the paper is on simple linear regression, we present the groundwork for expanding our two models to the multiple regression case.

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.

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