Journal articles: 'Large sample size'

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Lantz, Björn. "The large sample size fallacy." Scandinavian Journal of Caring Sciences 27, no. 2 (2012): 487–92. http://dx.doi.org/10.1111/j.1471-6712.2012.01052.x.

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Choi, Jai Won, and Balgobin Nandram. "Large Sample Problems." International Journal of Statistics and Probability 10, no. 2 (2021): 81. http://dx.doi.org/10.5539/ijsp.v10n2p81.

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Variance is very important in test statistics as it measures the degree of reliability of estimates. It depends not only on the sample size but also on other factors such as population size, type of data and its distribution, and method of sampling or experiments. But here, we assume that these other fasctors are fixed, and that the test statistic depends only on the sample size.  When the sample size is larger, the variance will be smaller. Smaller variance makes test statistics larger or gives more significant results in testing a hypothesis. Whatever the hypothesis is, it does not matter. Thus, the test result is often misleading because much of it reflects the sample size. Therefore, we discuss the large sample problem in performing traditional tests and show how to fix this problem.

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Byrne, Enda M., Anjali K. Henders, Ian B. Hickie, Christel M. Middeldorp, and Naomi R. Wray. "Nick Martin and the Genetics of Depression: Sample Size, Sample Size, Sample Size." Twin Research and Human Genetics 23, no. 2 (2020): 109–11. http://dx.doi.org/10.1017/thg.2020.13.

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AbstractNick Martin is a pioneer in recognizing the need for large sample size to study the complex, heterogeneous and polygenic disorders of common mental disorders. In the predigital era, questionnaires were mailed to thousands of twin pairs around Australia. Always quick to adopt new technology, Nick’s studies progressed to phone interviews and then online. Moreover, Nick was early to recognize the value of collecting DNA samples. As genotyping technologies improved over the years, these twin and family cohorts were used for linkage, candidate gene and genome-wide association studies. These cohorts have underpinned many analyses to disentangle the complex web of genetic and lifestyle factors associated with mental health. With characteristic foresight, Nick is chief investigator of our Australian Genetics of Depression Study, which has recruited 16,000 people with self-reported depression (plus DNA samples) over a time frame of a few months — analyses are currently ongoing. The mantra of sample size, sample size, sample size has guided Nick’s research over the last 30 years and continues to do so.

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Armstrong, Richard A. "Is there a large sample size problem?" Ophthalmic and Physiological Optics 39, no. 3 (2019): 129–30. http://dx.doi.org/10.1111/opo.12618.

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Kumar, A. "The Sample Size." Journal of Universal College of Medical Sciences 2, no. 1 (2014): 45–47. http://dx.doi.org/10.3126/jucms.v2i1.10493.

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Finding an "appropriate sample size" has been the most basic and foremost problem; a research worker is always faced with, in all sampling based analytical researches. This is so, since a very large sized sample results to unnecessary wastage of resources, while a very small sized sample may affect adversely the accuracy of sample estimates and thus in turn losing the very efficacy of selected sampling plan. The present paper attempts to highlight the main determinant factors and the analytical approach towards estimation ofrequired sample size, along with a few illustrations. DOI: http://dx.doi.org/10.3126/jucms.v2i1.10493 Journal of Universal College of Medical Sciences (2014) Vol.2(1): 45-47

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Kitikidou, K., and G. Chatzilazarou. "Estimating the sample size for fitting taper equations." Journal of Forest Science 54, No. 4 (2008): 176–82. http://dx.doi.org/10.17221/789-jfs.

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Much work has been done fitting taper equations to describe tree bole shapes, but few researchers have investigated how large the sample size should be. In this paper, a method that requires two variables that are linearly correlated was applied to determine the sample size for fitting taper equations. Two cases of sample size estimation were tested, based on the method mentioned above. In the first case, the sample size required is referred to the total number of diameters estimated in the sampled trees. In the second case, the sample size required is referred to the number of sampled trees. The analysis showed that both methods are efficient from a validity standpoint but the first method has the advantage of decreased cost, since it costs much more to incrementally sample another tree than it does to make another diameter measurement on an already sampled tree.

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Raudys, Šarūnas. "Trainable fusion rules. I. Large sample size case." Neural Networks 19, no. 10 (2006): 1506–16. http://dx.doi.org/10.1016/j.neunet.2006.01.018.

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Chittenden, Mark E. "Given a significance test, How large a sample size is large enough?" Fisheries 27, no. 8 (2002): 25–29. http://dx.doi.org/10.1577/1548-8446(2002)027<0025:gasthl>2.0.co;2.

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Barreiro-Ures, Daniel, Ricardo Cao, and Mario Francisco-Fernández. "Bandwidth Selection in Nonparametric Regression with Large Sample Size." Proceedings 2, no. 18 (2018): 1166. http://dx.doi.org/10.3390/proceedings2181166.

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In the context of nonparametric regression estimation, the behaviour of kernel methods such as the Nadaraya-Watson or local linear estimators is heavily influenced by the value of the bandwidth parameter, which determines the trade-off between bias and variance. This clearly implies that the selection of an optimal bandwidth, in the sense of minimizing some risk function (MSE, MISE, etc.), is a crucial issue. However, the task of estimating an optimal bandwidth using the whole sample can be very expensive in terms of computing time in the context of Big Data, due to the computational complexity of some of the most used algorithms for bandwidth selection (leave-one-out cross validation, for example, has O ( n 2 ) complexity). To overcome this problem, we propose two methods that estimate the optimal bandwidth for several subsamples of our large dataset and then extrapolate the result to the original sample size making use of the asymptotic expression of the MISE bandwidth. Preliminary simulation studies show that the proposed methods lead to a drastic reduction in computing time, while the statistical precision is only slightly decreased.

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Lara, L., W. D. Cotton, L. Feretti, et al. "A new sample of large angular size radio galaxies." Astronomy & Astrophysics 370, no. 2 (2001): 409–25. http://dx.doi.org/10.1051/0004-6361:20010254.

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Lara, L., I. Márquez, W. D. Cotton, et al. "A new sample of large angular size radio galaxies." Astronomy & Astrophysics 378, no. 3 (2001): 826–36. http://dx.doi.org/10.1051/0004-6361:20011279.

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Lara, L., G. Giovannini, W. D. Cotton, et al. "A new sample of large angular size radio galaxies." Astronomy & Astrophysics 421, no. 3 (2004): 899–911. http://dx.doi.org/10.1051/0004-6361:20035676.

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Heckmann, T., K. Gegg, A. Gegg, and M. Becht. "Sample size matters: investigating the effect of sample size on a logistic regression debris flow susceptibility model." Natural Hazards and Earth System Sciences Discussions 1, no. 3 (2013): 2731–79. http://dx.doi.org/10.5194/nhessd-1-2731-2013.

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Abstract. Predictive spatial modelling is an important task in natural hazard assessment and regionalisation of geomorphic processes or landforms. Logistic regression is a multivariate statistical approach frequently used in predictive modelling; it can be conducted stepwise in order to select from a number of candidate independent variables those that lead to the best model. In our case study on a debris flow susceptibility model, we investigate the sensitivity of model selection and quality to different sample sizes in light of the following problem: on the one hand, a sample has to be large enough to cover the variability of geofactors within the study area, and to yield stable results; on the other hand, the sample must not be too large, because a large sample is likely to violate the assumption of independent observations due to spatial autocorrelation. Using stepwise model selection with 1000 random samples for a number of sample sizes between n = 50 and n = 5000, we investigate the inclusion and exclusion of geofactors and the diversity of the resulting models as a function of sample size; the multiplicity of different models is assessed using numerical indices borrowed from information theory and biodiversity research. Model diversity decreases with increasing sample size and reaches either a local minimum or a plateau; even larger sample sizes do not further reduce it, and approach the upper limit of sample size given, in this study, by the autocorrelation range of the spatial datasets. In this way, an optimised sample size can be derived from an exploratory analysis. Model uncertainty due to sampling and model selection, and its predictive ability, are explored statistically and spatially through the example of 100 models estimated in one study area and validated in a neighbouring area: depending on the study area and on sample size, the predicted probabilities for debris flow release differed, on average, by 7 to 23 percentage points. In view of these results, we argue that researchers applying model selection should explore the behaviour of the model selection for different sample sizes, and that consensus models created from a number of random samples should be given preference over models relying on a single sample.

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Heckmann, T., K. Gegg, A. Gegg, and M. Becht. "Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows." Natural Hazards and Earth System Sciences 14, no. 2 (2014): 259–78. http://dx.doi.org/10.5194/nhess-14-259-2014.

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Abstract. Predictive spatial modelling is an important task in natural hazard assessment and regionalisation of geomorphic processes or landforms. Logistic regression is a multivariate statistical approach frequently used in predictive modelling; it can be conducted stepwise in order to select from a number of candidate independent variables those that lead to the best model. In our case study on a debris flow susceptibility model, we investigate the sensitivity of model selection and quality to different sample sizes in light of the following problem: on the one hand, a sample has to be large enough to cover the variability of geofactors within the study area, and to yield stable and reproducible results; on the other hand, the sample must not be too large, because a large sample is likely to violate the assumption of independent observations due to spatial autocorrelation. Using stepwise model selection with 1000 random samples for a number of sample sizes between n = 50 and n = 5000, we investigate the inclusion and exclusion of geofactors and the diversity of the resulting models as a function of sample size; the multiplicity of different models is assessed using numerical indices borrowed from information theory and biodiversity research. Model diversity decreases with increasing sample size and reaches either a local minimum or a plateau; even larger sample sizes do not further reduce it, and they approach the upper limit of sample size given, in this study, by the autocorrelation range of the spatial data sets. In this way, an optimised sample size can be derived from an exploratory analysis. Model uncertainty due to sampling and model selection, and its predictive ability, are explored statistically and spatially through the example of 100 models estimated in one study area and validated in a neighbouring area: depending on the study area and on sample size, the predicted probabilities for debris flow release differed, on average, by 7 to 23 percentage points. In view of these results, we argue that researchers applying model selection should explore the behaviour of the model selection for different sample sizes, and that consensus models created from a number of random samples should be given preference over models relying on a single sample.

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Sedlmeier, Peter, and Gerd Gigerenzer. "Intuitions about sample size: the empirical law of large numbers." Journal of Behavioral Decision Making 10, no. 1 (1997): 33–51. http://dx.doi.org/10.1002/(sici)1099-0771(199703)10:1<33::aid-bdm244>3.0.co;2-6.

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Durrant, J. D., D. L. Sabo, and R. J. Hyre. "Gender, Head Size, and ABRs Examined in Large Clinical Sample." Ear and Hearing 11, no. 3 (1990): 210–14. http://dx.doi.org/10.1097/00003446-199006000-00008.

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Slavin, Robert, and Dewi Smith. "The Relationship Between Sample Sizes and Effect Sizes in Systematic Reviews in Education." Educational Evaluation and Policy Analysis 31, no. 4 (2009): 500–506. http://dx.doi.org/10.3102/0162373709352369.

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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.

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Luo, Maoyi, Shan Xing, Yonggang Yang, et al. "Sequential analyses of actinides in large-size soil and sediment samples with total sample dissolution." Journal of Environmental Radioactivity 187 (July 2018): 73–80. http://dx.doi.org/10.1016/j.jenvrad.2018.01.028.

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Koch, Carl F. "Prediction of sample size effects on the measured temporal and geographic distribution patterns of species." Paleobiology 13, no. 1 (1987): 100–107. http://dx.doi.org/10.1017/s0094837300008617.

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Few paleontological studies of species distribution in time and space have adequately considered the effects of sample size. Most species occur very infrequently, and therefore sample size effects may be large relative to the faunal patterns reported. Examination of 10 carefully compiled large data sets (each more than 1,000 occurrences) reveals that the species-occurrence frequency distribution of each fits the log series distribution well and therefore sample size effects can be predicted. Results show that, if the materials used in assembling a large data set are resampled, as many as 25% of the species will not be found a second time even if both samples are of the same size. If the two samples are of unequal size, then the larger sample may have as many as 70% unique species and the smaller sample no unique species. The implications of these values are important to studies of species richness, origination, and extinction patterns, and biogeographic phenomena such as endemism or province boundaries. I provide graphs showing the predicted sample size effects for a range of data set size, species richness, and relative data size. For data sets that do not fit the log series distribution well, I provide example calculations and equations which are usable without a large computer. If these graphs or equations are not used, then I suggest that species which occur infrequently be eliminated from consideration. Studies in which sample size effects are not considered should include sample size information in sufficient detail that other workers might make their own evaluation of observed faunal patterns.

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Hinkle, Dennis E., J. Dale Oliver, and Charles A. Hinkle. "How Large Should the Sample Be? Part II—The One-Sample Case for Survey Research." Educational and Psychological Measurement 45, no. 2 (1985): 271–80. http://dx.doi.org/10.1177/001316448504500210.

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In a previous article, the authors discuss the importance of the effect size and the Type II error as factors in determining the sample size (Hinkle and Oliver, 1983). Tables were developed and presented for one-factor designs with k levels (2 ≤ k ≤ 8). However, between the time the article was submitted and its publication, the authors presented these tables at several national and regional meetings. A recurring question from colleagues attending these meetings was how these tables could be used for the one-sample case ( k = 1). Since they could not be, we were encouraged to develop comparable tables for the one-sample case. Thus, the purpose of this paper is to readdress the sample size question and to present these tables.

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Lazzeroni, L. C., and A. Ray. "The cost of large numbers of hypothesis tests on power, effect size and sample size." Molecular Psychiatry 17, no. 1 (2010): 108–14. http://dx.doi.org/10.1038/mp.2010.117.

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Omeroglu, P. Yolci, Á. Ambrus, D. Boyacioglu, and E. Solymosne Majzik. "Uncertainty of the sample size reduction step in pesticide residue analysis of large-sized crops." Food Additives & Contaminants: Part A 30, no. 1 (2013): 116–26. http://dx.doi.org/10.1080/19440049.2012.728720.

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Chandrasekharan, Subramanian, Jayadevan Sreedharan, and Aji Gopakumar. "Statistical Issues in Small and Large Sample: Need of Optimum Upper Bound for the Sample Size." International Journal of Computational & Theoretical Statistics 06, no. 02 (2019): 108–18. http://dx.doi.org/10.12785/ijcts/060201.

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Qin, S., and G. E. O. Widera. "Determination of Sample Size in Service Inspection." Journal of Pressure Vessel Technology 119, no. 1 (1997): 57–60. http://dx.doi.org/10.1115/1.2842267.

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When performing inservice inspection on a large volume of identical components, it becomes an almost impossible task to inspect all those in which defects may exist, even if their failure probabilities are known. As a result, an appropriate sample size needs to be determined when setting up an inspection program. In this paper, a probabilistic analysis method is employed to solve this problem. It is assumed that the characteristic data of components has a certain distribution which can be taken as known when the mean and standard deviations of serviceable and defective sets of components are estimated. The sample size can then be determined within an acceptable assigned error range. In this way, both false rejection and acceptance can be avoided with a high degree of confidence.

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PALMER, R. G., and JOAN ADLER. "GROUND STATES FOR LARGE SAMPLES OF TWO-DIMENSIONAL ISING SPIN GLASSES." International Journal of Modern Physics C 10, no. 04 (1999): 667–75. http://dx.doi.org/10.1142/s0129183199000504.

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We have developed a combinatoric matching method to find the exact groundstate energy for the 2-D Ising spin glass with the ± J distribution and equal numbers of positive and negative bonds. For the largest size (1800×1800 plaquettes of spins), we averaged results from 278 samples and for the smaller ones up to 374, 375 samples. We also studied the behavior of the distributions of computer time (CPU) and memory as functions of sample size. We present finite size scaling leading to a groundstate energy estimate of E∞=-1.40193±2 for the infinite system. We found that the memory scales as the square of sample length and that for a given size, the CPU time appears to have a skewed and high-tailed distribution.

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Tseng, Chi-Hong, and Yongzhao Shao. "Sample Size Growth with an Increasing Number of Comparisons." Journal of Probability and Statistics 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/935621.

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An appropriate sample size is crucial for the success of many studies that involve a large number of comparisons. Sample size formulas for testing multiple hypotheses are provided in this paper. They can be used to determine the sample sizes required to provide adequate power while controlling familywise error rate or false discovery rate, to derive the growth rate of sample size with respect to an increasing number of comparisons or decrease in effect size, and to assess reliability of study designs. It is demonstrated that practical sample sizes can often be achieved even when adjustments for a large number of comparisons are made as in many genomewide studies.

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Kovacevic, R., and Y. M. Zhang. "Identification of Surface Characteristics from Large Samples." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 206, no. 4 (1992): 275–84. http://dx.doi.org/10.1243/pime_proc_1992_206_127_02.

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Surface roughness characteristics have been modelled by autoregressive moving average (ARMA) models. Frequently, extra-large samples from the surface are available. Due to the non-linearity and the computational burden dependence on sample size, the available data can not be sufficiently utilized to fit ARMA models in most cases. In an attempt to sufficiently employ the available data, an innovative ARMA identification approach is presented. The computational burden of this approach is nearly independent of the sample size. The accuracy ratio between the present approach and the non-linear least squares algorithm is determined. Both simulation and application have been conducted to confirm its effectiveness.

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Lan, KK Gordan, and Janet T. Wittes. "Some thoughts on sample size: A Bayesian-frequentist hybrid approach." Clinical Trials 9, no. 5 (2012): 561–69. http://dx.doi.org/10.1177/1740774512453784.

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Background Traditional calculations of sample size do not formally incorporate uncertainty about the likely effect size. Use of a normal prior to express that uncertainty, as recently recommended, can lead to power that does not approach 1 as the sample size approaches infinity. Purpose To provide approaches for calculating sample size and power that formally incorporate uncertainty about effect size. The relevant formulas should ensure that power approaches one as sample size increases indefinitely and should be easy to calculate. Methods We examine normal, truncated normal, and gamma priors for effect size computationally and demonstrate analytically an approach to approximating the power for a truncated normal prior. We also propose a simple compromise method that requires a moderately larger sample size than the one derived from the fixed effect method. Results Use of a realistic prior distribution instead of a fixed treatment effect is likely to increase the sample size required for a Phase 3 trial. The standard fixed effect method for moving from estimates of effect size obtained in a Phase 2 trial to the sample size of a Phase 3 trial ignores the variability inherent in the estimate from Phase 2. Truncated normal priors appear to require unrealistically large sample sizes while gamma priors appear to place too much probability on large effect sizes and therefore produce unrealistically high power. Limitations The article deals with a few examples and a limited range of parameters. It does not deal explicitly with binary or time-to-failure data. Conclusions Use of the standard fixed approach to sample size calculation often yields a sample size leading to lower power than desired. Other natural parametric priors lead either to unacceptably large sample sizes or to unrealistically high power. We recommend an approach that is a compromise between assuming a fixed effect size and assigning a normal prior to the effect size.

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Blair, R. Clifford, and James J. Higgins. "A Comparison of the Power of the Paired Samples Rank Transform Statistic to that of Wilcoxon’s Signed Ranks Statistic." Journal of Educational Statistics 10, no. 4 (1985): 368–83. http://dx.doi.org/10.3102/10769986010004368.

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This study was concerned with the effects of reliability of observations, sample size, magnitudes of treatment effects, and the shape of the sampled population on the relative power of the paired samples rank transform statistic and Wilcoxon’s signed ranks statistic. It was found that factors favoring the Wilcoxon statistic were high reliability of observations, moderate to large sample sizes, and small treatment effects. Factors favoring the rank transform statistic were low reliability of observations, small sample size, and moderate to large treatment effects. It was also noted that the Wilcoxon statistic appeared to maintain the power advantage under normal theory assumptions.

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Kuswanto, Heri, Ayu Asfihani, Yogi Sarumaha, and Hayato Ohwada. "Logistic Regression Ensemble for Predicting Customer Defection with Very Large Sample Size." Procedia Computer Science 72 (2015): 86–93. http://dx.doi.org/10.1016/j.procs.2015.12.108.

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Yin, Qingbo, Ehsan Adeli, Liran Shen, and Dinggang Shen. "Population-guided large margin classifier for high-dimension low-sample-size problems." Pattern Recognition 97 (January 2020): 107030. http://dx.doi.org/10.1016/j.patcog.2019.107030.

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MASSARE, JUDY A., and DEAN R. LOMAX. "Hindfins of Ichthyosaurus: effects of large sample size on ‘distinct’ morphological characters." Geological Magazine 156, no. 4 (2018): 725–44. http://dx.doi.org/10.1017/s0016756818000146.

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AbstractThe abundance of specimens of Ichthyosaurus provides an opportunity to assess morphological variation without the limits of a small sample size. This research evaluates the variation and taxonomic utility of hindfin morphology. Two seemingly distinct morphotypes of the mesopodium occur in the genus. Morphotype 1 has three elements in the third row: metatarsal two, distal tarsal three and distal tarsal four. This is the common morphology in Ichthyosaurus breviceps, I. conybeari and I. somersetensis. Morphotype 2 has four elements in the third row, owing to a bifurcation. This morphotype occurs in at least some specimens of each species, but it has several variations distinguished by the extent of contact of elements in the third row with the astragalus. Two specimens display a different morphotype in each fin, suggesting that the difference reflects individual variation. In Ichthyosaurus, the hindfin is taxonomically useful at the genus level, but species cannot be identified unequivocally from a well-preserved hindfin, although certain morphologies are more common in certain species than others. The large sample size filled in morphological gaps between what initially appeared to be taxonomically distinct characters. The full picture of variation would have been obscured with a small sample size. Furthermore, we have found several unusual morphologies which, in isolation, could have been mistaken for new taxa. Thus, one must be cautious when describing new species or genera on the basis of limited material, such as isolated fins and fragmentary specimens.

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Zaharov, V. V., and M. Teixeira. "SMI-MVDR Beamformer Implementations for Large Antenna Array and Small Sample Size." IEEE Transactions on Circuits and Systems I: Regular Papers 55, no. 10 (2008): 3317–27. http://dx.doi.org/10.1109/tcsi.2008.925380.

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Altay, G. "Empirically determining the sample size for large-scale gene network inference algorithms." IET Systems Biology 6, no. 2 (2012): 35. http://dx.doi.org/10.1049/iet-syb.2010.0091.

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Wang, Zhang-Jie, Qing-Jie Li, Zhi-Wei Shan, Ju Li, Jun Sun, and Evan Ma. "Sample size effects on the large strain bursts in submicron aluminum pillars." Applied Physics Letters 100, no. 7 (2012): 071906. http://dx.doi.org/10.1063/1.3681582.

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Sen, Ashish. "Large Sample-Size Distribution of Statistics Used In Testing for Spatial Correlation." Geographical Analysis 8, no. 2 (2010): 175–84. http://dx.doi.org/10.1111/j.1538-4632.1976.tb01066.x.

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Zhang, Chaosheng, Frank T. Manheim, John Hinde, and Jeffrey N. Grossman. "Statistical characterization of a large geochemical database and effect of sample size." Applied Geochemistry 20, no. 10 (2005): 1857–74. http://dx.doi.org/10.1016/j.apgeochem.2005.06.006.

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McClymont, Juliet, Russell Savage, Todd C. Pataky, Robin Crompton, James Charles, and Karl T. Bates. "Intra-subject sample size effects in plantar pressure analyses." PeerJ 9 (June 24, 2021): e11660. http://dx.doi.org/10.7717/peerj.11660.

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Background Recent work using large datasets (>500 records per subject) has demonstrated seemingly high levels of step-to-step variation in peak plantar pressure within human individuals during walking. One intuitive consequence of this variation is that smaller sample sizes (e.g., 10 steps per subject) may be quantitatively and qualitatively inaccurate and fail to capture the variance in plantar pressure of individuals seen in larger data sets. However, this remains quantitatively unexplored reflecting a lack of detailed investigation of intra-subject sample size effects in plantar pressure analysis. Methods Here we explore the sensitivity of various plantar pressure metrics to intra-subject sample size (number of steps per subject) using a random subsampling analysis. We randomly and incrementally subsample large data sets (>500 steps per subject) to compare variability in three metric types at sample sizes of 5–400 records: (1) overall whole-record mean and maximum pressure; (2) single-pixel values from five locations across the foot; and (3) the sum of pixel-level variability (measured by mean square error, MSE) from the whole plantar surface. Results Our results indicate that the central tendency of whole-record mean and maximum pressure within and across subjects show only minor sensitivity to sample size >200 steps. However, <200 steps, and particularly <50 steps, the range of overall mean and maximum pressure values yielded by our subsampling analysis increased considerably resulting in potential qualitative error in analyses of pressure changes with speed within-subjects and in comparisons of relative pressure magnitudes across subjects at a given speed. Our analysis revealed considerable variability in the absolute and relative response of the single pixel centroids of five regions to random subsampling. As the number of steps analysed decreased, the absolute value ranges were highest in the areas of highest pressure (medial forefoot and hallux), while the largest relative changes were seen in areas of lower pressure (the midfoot). Our pixel-level measure of variability by MSE across the whole-foot was highly sensitive to our manipulation of sample size, such that the range in MSE was exponentially larger in smaller subsamples. Random subsampling showed that the range in pixel-level MSE only came within 5% of the overall sample size in subsamples of >400 steps. The range in pixel-level MSE at low subsamples (<50) was 25–75% higher than that of the full datasets of >500 pressure records per subject. Overall, therefore, we demonstrate a high probability that the very small sample sizes (n < 20 records), which are routinely used in human and animal studies, capture a relatively low proportion of variance evident in larger plantar pressure data set, and thus may not accurately reflect the true population mean.

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Bradley, M. T., and A. Brand. "Alpha Values as a Function of Sample Size, Effect Size, and Power: Accuracy over Inference." Psychological Reports 112, no. 3 (2013): 835–44. http://dx.doi.org/10.2466/03.49.pr0.112.3.835-844.

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Tables of alpha values as a function of sample size, effect size, and desired power were presented. The tables indicated expected alphas for small, medium, and large effect sizes given a variety of sample sizes. It was evident that sample sizes for most psychological studies are adequate for large effect sizes defined at .8. The typical alpha level of .05 and desired power of 90% can be achieved with 70 participants in two groups. It was perhaps doubtful if these ideal levels of alpha and power have generally been achieved for medium effect sizes in actual research, since 170 participants would be required. Small effect sizes have rarely been tested with an adequate number of participants or power. Implications were discussed.

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Zhu, Hong, Song Zhang, and Chul Ahn. "Sample size considerations for split-mouth design." Statistical Methods in Medical Research 26, no. 6 (2015): 2543–51. http://dx.doi.org/10.1177/0962280215601137.

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Split-mouth designs are frequently used in dental clinical research, where a mouth is divided into two or more experimental segments that are randomly assigned to different treatments. It has the distinct advantage of removing a lot of inter-subject variability from the estimated treatment effect. Methods of statistical analyses for split-mouth design have been well developed. However, little work is available on sample size consideration at the design phase of a split-mouth trial, although many researchers pointed out that the split-mouth design can only be more efficient than a parallel-group design when within-subject correlation coefficient is substantial. In this paper, we propose to use the generalized estimating equation (GEE) approach to assess treatment effect in split-mouth trials, accounting for correlations among observations. Closed-form sample size formulas are introduced for the split-mouth design with continuous and binary outcomes, assuming exchangeable and “nested exchangeable” correlation structures for outcomes from the same subject. The statistical inference is based on the large sample approximation under the GEE approach. Simulation studies are conducted to investigate the finite-sample performance of the GEE sample size formulas. A dental clinical trial example is presented for illustration.

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Zedaker, S. M., T. G. Gregoire, and J. H. Miller. "Sample-size needs for forestry herbicide trials." Canadian Journal of Forest Research 23, no. 10 (1993): 2153–58. http://dx.doi.org/10.1139/x93-268.

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Forest herbicide experiments are increasingly being designed to evaluate smaller treatment differences when comparing existing effective treatments, tank mix ratios, surfactants, and new low-rate products. The ability to detect small differences in efficacy is dependent upon the relationship among sample size, type I and II error probabilities, and the coefficients of variation of the efficacy data. The common sources of variation in efficacy measurements and design considerations for controlling variation are reviewed, while current shortcomings are clarified. A summary of selected trials estimates that coefficients of variation often range between 25 and 100%, making the number of observations necessary to detect small differences very large, especially when the power of the test (1–β) is considered. Very often the power of the test has been ignored when designing experiments because of the difficulty in calculating β. An available program for microcomputers is introduced that allows researchers to examine relationships among sample size, effect size, and coefficients of variation for specified designs, α and β. This program should aid investigators in planning studies that optimize experimental power to detect anticipated effect sizes within resource constraints.

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Sheppard, Charles R. C. "How Large should my Sample be? Some Quick Guides to Sample Size and the Power of Tests." Marine Pollution Bulletin 38, no. 6 (1999): 439–47. http://dx.doi.org/10.1016/s0025-326x(99)00048-x.

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Nadakuditi, Raj, and Arthur B. Baggeroer. "Analytical prediction of sample eigenvector quality deterioration in large arrays due to SNR or sample size constraints." Journal of the Acoustical Society of America 123, no. 5 (2008): 3334. http://dx.doi.org/10.1121/1.2933860.

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Zhi, Wei Mei, Hua Ping Guo, and Ming Fan. "Sample Size on the Impact of Imbalance Learning." Advanced Materials Research 756-759 (September 2013): 2547–51. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2547.

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Classification of imbalanced data sets is widely used in many real life applications. Most state-of-the-art classification methods which assume the data sets are relatively balanced lose their efficiency. The paper discusses the factors which influence the modeling of a capable classifier in identifying rare events, especially for the factor of sample size. Carefully designed experiments using Rotation Forest as base classifier, carried on 3 datasets from UCI Machine Learning Repository based on weak show that, in particular imbalance ratio, increases the size of training set by unsupervised resample the large error rate caused by the imbalanced class distribution decreases. The common classification algorithm can reach good effect.

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Roldán Ahumada, Judith Agueda, and Martha Lorena Avendaño Garrido. "A Commentary on Diversity Measures UniFrac in Very Small Sample Size." Evolutionary Bioinformatics 15 (January 2019): 117693431984351. http://dx.doi.org/10.1177/1176934319843515.

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In phylogenetic, the diversity measures as UniFrac, weighted UniFrac, and normalized weighted UniFrac are used to estimate the closeness between two samples of genetic material sequences. These measures are widely used in microbiology to compare microbial communities. Furthermore, when the sample size is large enough, very good results have been obtained experimentally. However, some authors do not suggest using them when the sample size is very small. Recently, it has been mentioned that the weighted UniFrac measure can be seen as the Kantorovich-Rubinstein metric between the corresponding empirical distributions of samples of genetic material. Also, it is well known that the Kantorovich-Rubinstein metric complies the metric definition. However, one of the main reasons to establish it is that the sample size is large enough. The goal of this article is to prove that the diversity measures UniFrac are not metrics when the sample size is very small, which justifies why it must not be used in that case, but yes the Kantorovich-Rubinstein metric.

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Johnson, Roger W., and Donna V. Kliche. "Large Sample Comparison of Parameter Estimates in Gamma Raindrop Distributions." Atmosphere 11, no. 4 (2020): 333. http://dx.doi.org/10.3390/atmos11040333.

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Raindrop size distributions have been characterized through the gamma family. Over the years, quite a few estimates of these gamma parameters have been proposed. The natural question for the practitioner, then, is what estimation procedure should be used. We provide guidance in answering this question when a large sample size (>2000 drops) of accurately measured drops is available. Seven estimation procedures from the literature: five method of moments procedures, maximum likelihood, and a pseudo maximum likelihood procedure, were examined. We show that the two maximum likelihood procedures provide the best precision (lowest variance) in estimating the gamma parameters. Method of moments procedures involving higher-order moments, on the other hand, give rise to poor precision (high variance) in estimating these parameters. A technique called the delta method assisted in our comparison of these various estimation procedures.

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Yang, Y., Tomonrori Kitashima, T. Hara, et al. "Effect of Grain Size on Oxidation Resistance of Unalloyed Titanium." Materials Science Forum 879 (November 2016): 2187–91. http://dx.doi.org/10.4028/www.scientific.net/msf.879.2187.

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The effect of the grain size on the high-temperature oxidation resistance of unalloyed titanium was experimentally investigated using titanium samples with two different grain sizes of 219 μm and 118 μm. The weight gain during oxidation and the penetration depth of oxygen from a metal surface were larger in the small-grain-size sample compared with the large-grain-size sample. In addition, oxygen diffusion was faster in the substrate of the small-grain-size sample. These results were attributed to the grain-boundary diffusion of oxygen. A steep change in the oxygen concentration was observed at a grain boundary. Our simulation results suggested that slower oxygen diffusion into the inner grain from the surface through the grain boundary with high diffusivity can cause the observed steep change in the oxygen concentration.

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Vasilopoulou, T., F. Tzika, and I. E. Stamatelatos. "Large Sample Neutron Activation Analysis: Developments and Perspectives." HNPS Proceedings 19 (January 1, 2020): 100. http://dx.doi.org/10.12681/hnps.2522.

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Most of the available analytical techniques do not comply with the need for direct trace element analysis of samples of mass exceeding the order of grams. Instead sub-sampling methods are used to obtain representative sampling of the studied material. Large Sample Neutron Activation Analysis (LSNAA) is a powerful technique, which can fulfill this need in a non-destructive way, free of sample size restrictions due to the high penetrating properties of neutrons and gamma rays in matter. However, corrections are required in order to obtain quantitative analysis results. Due to its distinct advantage to allow for the analysis of whole objects, LSNAA has found successful applications in diverse fields of science and technology. In the present study, the LSNAA method and applications representative of the capabilities of the technique are presented. Moreover, recent developments and future perspectives of the technique are discussed.

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Diniz, Maria Cristina Duarte Rios, César Augusto Brasil Pereira Pinto, and Eduardo de Souza Lambert. "Sample size for family evaluation in potato breeding programs." Ciência e Agrotecnologia 30, no. 2 (2006): 277–82. http://dx.doi.org/10.1590/s1413-70542006000200013.

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Clonal families from a broad genetic base population in the Potato Breeding Program at the Universidade Federal de Lavras (UFLA), Brazil, were used in this trials. Twenty-five families were assessed in a 5 x 5 triple lattice design. Each plot consisted of 30 clones distributed in three rows of ten plants. Tuber yield per plant, percentage of large tubers, mean weight of large tubers, mean medium-sized tuber weight and tuber specific gravity were measured. Three hundred experiments were simulated varying the family sizes from three to 90 clones. The coefficients of experimental variation (CVe), the coefficients of genetic variation (CVg), heritabilities for family mean and the CVg/CVe ratio were estimated. Genetic parameters were stabilized with family sizes as small as six clones, depending on the trait. This indicates that the families can be adequately represented by a small sample of clones. Using the maximum curvature method it is possible to conclude that approximately 30 clones would be sufficient to represent each family, even for traits with the highest CVe. The genetic variance within family was greater than the genetic variance among families for all traits, indicating a favorable potential for within family selection. The correlation coefficients of the family means with the 5%, 10%, 15% and 20% best clones from each family, considering the five traits assessed, were always high, meaning that within the best families generally are the best clones.

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Chauvet, Guillaume. "Large sample properties of the Midzuno sampling scheme with probabilities proportional to size." Statistics & Probability Letters 159 (April 2020): 108680. http://dx.doi.org/10.1016/j.spl.2019.108680.

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Journal articles on the topic 'Large sample size'

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