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Journal articles on the topic 'Small sample size'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 June 2025

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1

Yang, Shengping, and Gilbert Berdine. ""Small" sample size." Southwest Respiratory and Critical Care Chronicles 11, no. 49 (2023): 52–55. http://dx.doi.org/10.12746/swrccc.v11i49.1251.

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2

Student. "SMALL SAMPLE SIZE SCIENTIST." Pediatrics 83, no. 3 (1989): A72. http://dx.doi.org/10.1542/peds.83.3.a72a.

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The believer in the law of small numbers practices science as follows: 1. He gambles his research hypotheses on small samples without realizing that the odds against him are unreasonably high. He overestimates power. 2. He has undue confidence in early trends (e.g., the data of the first few subjects) and in the stability of observed patterns (e.g., the number and identity of significant results). He overestimates significance. 3. In evaluating replications, his or others', he has unreasonably high expectations about the replicability of significant results. He underestimates the breadth of confidence intervals. 4. He rarely attributes a deviation of results from expectations to sampling variability, because he finds a causal "explanation" for any discrepancy. Thus, he has little opportunity to recognize sampling variation in action. His belief in the law of small numbers, therefore, will forever remain intact.
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3

Van Oort, Michiel J. M., and Mary Anne White. "Automated, small sample‐size adiabatic calorimeter." Review of Scientific Instruments 58, no. 7 (1987): 1239–41. http://dx.doi.org/10.1063/1.1139445.

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4

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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5

Korn, Edward L. "Sample Size Tables for Bounding Small Proportions." Biometrics 42, no. 1 (1986): 213. http://dx.doi.org/10.2307/2531259.

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6

VanSant, Ann F. "The Dilemma of the Small Sample Size." Pediatric Physical Therapy 15, no. 3 (2003): 145. http://dx.doi.org/10.1097/01.pep.0000087995.07869.49.

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7

Khobragade, Vandana, M. S. Pradeep Kumar Patnaik, and Srinivasa Rao Sura. "Revaluating Pretraining in Small Size Training Sample Regime." International Journal of Electrical and Electronics Research 10, no. 3 (2022): 694–704. http://dx.doi.org/10.37391/ijeer.100346.

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Deep neural network (DNN) based models are highly acclaimed in medical image classification. The existing DNN architectures are claimed to be at the forefront of image classification. These models require very large datasets to classify the images with a high level of accuracy. However, fail to perform when trained on datasets of small size. Low accuracy and overfitting are the problems observed when medical datasets of small sizes are used to train a classifier using deep learning models such as Convolutional Neural Networks (CNN). These existing methods and models either always overfit when training on these small datasets or will result in classification accuracy which tends towards randomness. This issue stands even when using Transfer Learning (TL), the current standard for such a scenario. In this paper, we have tested several models including ResNet and VGGs along with more modern models like MobileNets on different medical datasets with transfer learning and without transfer learning. We have proposed solid theories as to why there exists a need for a more novel approach to this issue, and how the current methodologies fail when applied to the aforementioned datasets. Larger, more complex models are not able to converge for smaller datasets. Smaller models with less complexity perform better on the same dataset than their larger model counterparts.
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8

Kuo, Bor-Chen, and Kuang-Yu Chang. "Feature Extractions for Small Sample Size Classification Problem." IEEE Transactions on Geoscience and Remote Sensing 45, no. 3 (2007): 756–64. http://dx.doi.org/10.1109/tgrs.2006.885074.

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9

Rugini, Luca, Paolo Banelli, and Geert Leus. "Small Sample Size Performance of the Energy Detector." IEEE Communications Letters 17, no. 9 (2013): 1814–17. http://dx.doi.org/10.1109/lcomm.2013.080813.131399.

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10

Bacchetti, Peter. "Small sample size is not the real problem." Nature Reviews Neuroscience 14, no. 8 (2013): 585. http://dx.doi.org/10.1038/nrn3475-c3.

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11

Piovesana, Adina, and Graeme Senior. "How Small Is Big: Sample Size and Skewness." Assessment 25, no. 6 (2016): 793–800. http://dx.doi.org/10.1177/1073191116669784.

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12

Raudys, Šarūnas. "Trainable fusion rules. II. Small sample-size effects." Neural Networks 19, no. 10 (2006): 1517–27. http://dx.doi.org/10.1016/j.neunet.2006.01.019.

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13

Wang, Yu, Zheng Guan, and Tengyuan Zhao. "Sample size determination in geotechnical site investigation considering spatial variation and correlation." Canadian Geotechnical Journal 56, no. 7 (2019): 992–1002. http://dx.doi.org/10.1139/cgj-2018-0474.

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Site investigation is a fundamental element in geotechnical engineering practice, but only a small portion of geomaterials is sampled and tested during site investigation. This leads to a question of sample size determination: how many samples are needed to achieve a target level of accuracy for the results inferred from the samples? Sample size determination is a well-known topic in statistics and has many applications in a wide variety of areas. However, conventional statistical methods, which mainly deal with independent data, only have limited applications in geotechnical site investigation because geotechnical data are not independent, but spatially varying and correlated. Existing design codes around the world (e.g., Eurocode 7) only provide conceptual principles on sample size determination. No scientific or quantitative method is available for sample size determination in site investigation considering spatial variation and correlation of geotechnical properties. This study performs an extensive parametric study and develops a statistical chart for sample size determination with consideration of spatial variation and correlation using Bayesian compressive sensing or sampling. Real cone penetration test data and real laboratory test data are used to illustrate application of the proposed statistical chart, and the method is shown to perform well.
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14

O'Neil, Dennis H. "Excavation Sample Size: A Cautionary Tale." American Antiquity 58, no. 3 (1993): 523–29. http://dx.doi.org/10.2307/282111.

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Frequently, only five percent or less of a midden site is excavated for environmental-analysis purposes before it is turned over to the bulldozers for destruction. Such exceptionally small sample sizes have become accepted in cultural-resource-management work as adequate for gaining a good understanding of the chronology and cultural activities at a site. This assumption was tested by the author with a 63 percent excavation sampling fraction from a southern California midden. The data indicate that a far-from-complete understanding of a site may result from small sampling fractions and that more carefully designed sampling strategies and statistical manipulation of the data may not overcome this problem.
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15

Ishibashi, Hideaki, Kazushi Higa, and Tetsuo Furukawa. "Multi-task manifold learning for small sample size datasets." Neurocomputing 473 (February 2022): 138–57. http://dx.doi.org/10.1016/j.neucom.2021.11.043.

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16

Collins, Michael W., and Scott B. Morris. "Testing for adverse impact when sample size is small." Journal of Applied Psychology 93, no. 2 (2008): 463–71. http://dx.doi.org/10.1037/0021-9010.93.2.463.

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17

Wang, Jie, K. N. Plataniotis, Juwei Lu, and A. N. Venetsanopoulos. "Kernel quadratic discriminant analysis for small sample size problem." Pattern Recognition 41, no. 5 (2008): 1528–38. http://dx.doi.org/10.1016/j.patcog.2007.10.024.

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18

Wang, Su-Jing, Hui-Ling Chen, Xu-Jun Peng, and Chun-Guang Zhou. "Exponential locality preserving projections for small sample size problem." Neurocomputing 74, no. 17 (2011): 3654–62. http://dx.doi.org/10.1016/j.neucom.2011.07.007.

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19

Bray, George A. "The obesity paradox—an artifact of small sample size?" Nature Reviews Cardiology 6, no. 9 (2009): 561–62. http://dx.doi.org/10.1038/nrcardio.2009.140.

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20

Fitts, D. A. "Increased sample size efficiency in small N animal research." Appetite 52, no. 3 (2009): 831. http://dx.doi.org/10.1016/j.appet.2009.04.073.

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21

Christley, R. M. "Statistical significance, power and sample size – what does it all mean?" Journal of Small Animal Practice 49, no. 6 (2008): 263. http://dx.doi.org/10.1111/j.1748-5827.2008.00612.x.

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22

Fouad, Ahmed M., Mohamed Saleh, and Amir F. Atiya. "A Novel Quota Sampling Algorithm for Generating Representative Random Samples given Small Sample Size." International Journal of System Dynamics Applications 2, no. 1 (2013): 97–113. http://dx.doi.org/10.4018/ijsda.2013010105.

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In this paper, a novel algorithm is proposed for sampling from discrete probability distributions using the probability proportional to size sampling method, which is a special case of Quota sampling method. The motivation for this study is to devise an efficient sampling algorithm that can be used in stochastic optimization problems -- when there is a need to minimize the sample size. Several experiments have been conducted to compare the proposed algorithm with two widely used sample generation methods, the Monte Carlo using inverse transform, and quasi-Monte Carlo algorithms. The proposed algorithm gave better accuracy than these methods, and in terms of time complexity it is nearly of the same order.
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23

Faes, Christel, Geert Molenberghs, Marc Aerts, Geert Verbeke, and Michael G. Kenward. "The Effective Sample Size and an Alternative Small-Sample Degrees-of-Freedom Method." American Statistician 63, no. 4 (2009): 389–99. http://dx.doi.org/10.1198/tast.2009.08196.

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24

Wu, Yujun, Marc G. Genton, and Leonard A. Stefanski. "A Multivariate Two-Sample Mean Test for Small Sample Size and Missing Data." Biometrics 62, no. 3 (2006): 877–85. http://dx.doi.org/10.1111/j.1541-0420.2006.00533.x.

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25

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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26

Faber, Jorge, and Lilian Martins Fonseca. "How sample size influences research outcomes." Dental Press Journal of Orthodontics 19, no. 4 (2014): 27–29. http://dx.doi.org/10.1590/2176-9451.19.4.027-029.ebo.

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Sample size calculation is part of the early stages of conducting an epidemiological, clinical or lab study. In preparing a scientific paper, there are ethical and methodological indications for its use. Two investigations conducted with the same methodology and achieving equivalent results, but different only in terms of sample size, may point the researcher in different directions when it comes to making clinical decisions. Therefore, ideally, samples should not be small and, contrary to what one might think, should not be excessive. The aim of this paper is to discuss in clinical language the main implications of the sample size when interpreting a study.
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27

Luo, Jia, and BJ Fox. "A Review of the Mantel Test in Dietary Studies: Effect of Sample Size and Inequality of Sample Sizes." Wildlife Research 23, no. 3 (1996): 267. http://dx.doi.org/10.1071/wr9960267.

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The Mantel test has been widely used in many areas of research in biological science since its publication in 1967 and is particularly well suited to use in dietary studies. It is a non-parametric test that has been suggested as appropriate for comparisons when sample sizes are small. The methodology is reviewed, benefits to be gained are examined, and effects of features that have considerable impact (sample-size dependence and sensitivity to inequality of sample size) are considered.
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28

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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29

Bao, Yan, Frank Heilig, Chuo-Hsuan Lee, and Edward J. Lusk. "Full Range Testing of the Small Size Effect Bias for Benford Screening: A Note." International Journal of Economics and Finance 10, no. 6 (2018): 47. http://dx.doi.org/10.5539/ijef.v10n6p47.

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Bao, Lee, Heilig, and Lusk (2018) have documented and illustrated the Small Sample Size bias in Benford Screening of datasets for Non-Conformity. However, their sampling plan tested only a few random sample-bundles from a core set of data that were clearly Conforming to the Benford first digit profile. We extended their study using the same core datasets and DSS, called the Newcomb Benford Decision Support Systems Profiler [NBDSSP], to create an expanded set of random samples from their core sample. Specifically, we took repeated random samples in blocks of 10 down to 5% from their core-set of data in increments of 5% and finished with a random sample of 1%, 0.5% & 20 thus creating 221 sample-bundles. This arm focuses on the False Positive Signaling Error [FPSE]—i.e., believing that the sampled dataset is Non-Conforming when it, in fact, comes from a Conforming set of data. The second arm used the Hill Lottery dataset, argued and tested as Non-Conforming; we will use the same iteration model noted above to create a test of the False Negative Signaling Error [FNSE]—i.e., if for the sampled datasets the NBDSSP fails to detect Non-Conformity—to wit believing incorrectly that the dataset is Conforming. We find that there is a dramatic point in the sliding sampling scale at about 120 sampled points where the FPSE first appears—i.e., where the state of nature: Conforming incorrectly is flagged as Non-Conforming. Further, we find it is very unlikely that the FNSE manifests itself for the Hill dataset. This demonstrated clearly that small datasets are indeed likely to create the FPSE, and there should be little concern that Hill-type of datasets will not be indicated as Non-Conforming. We offer a discussion of these results with implications for audits in the Big-Data context where the audit In-charge may find it necessary to partition the datasets of the client.
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Costanzo, Salvatore, Rossana Pasquino, Jörg Läuger, and Nino Grizzuti. "Milligram Size Rheology of Molten Polymers." Fluids 4, no. 1 (2019): 28. http://dx.doi.org/10.3390/fluids4010028.

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During laboratory practice, it is often necessary to perform rheological measurements with small specimens, mainly due to the limited availability of the investigated systems. Such a restriction occurs, for example, because the laboratory synthesis of new materials is performed on small scales, or can concern biological samples that are notoriously difficult to be extracted from living organisms. A complete rheological characterization of a viscoelastic material involves both linear and nonlinear measurements. The latter are more challenging and generally require more mass, as flow instabilities often cause material losses during the experiments. In such situations, it is crucial to perform rheological tests carefully in order to avoid experimental artifacts caused by the use of small geometries. In this paper, we indicate the drawbacks of performing linear and nonlinear rheological measurements with very small amounts of samples, and by using a well-characterized linear polystyrene, we attempt to address the challenge of obtaining reliable measurements with sample masses of the order of a milligram, in both linear and nonlinear regimes. We demonstrate that, when suitable protocols and careful running conditions are chosen, linear viscoelastic mastercurves can be obtained with good accuracy and reproducibility, working with plates as small as 3 mm in diameter and sample thickness of less than 0.2 mm. This is equivalent to polymer masses of less than 2 mg. We show also that the nonlinear start-up shear fingerprint of polymer melts can be reliably obtained with samples as small as 10 mg.
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31

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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32

Xie, Xuan, Hui Feng, and Bo Hu. "Bandwidth Detection of Graph Signals with a Small Sample Size." Sensors 21, no. 1 (2020): 146. http://dx.doi.org/10.3390/s21010146.

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Bandwidth is the crucial knowledge to sampling, reconstruction or estimation of the graph signal (GS). However, it is typically unknown in practice. In this paper, we focus on detecting the bandwidth of bandlimited GS with a small sample size, where the number of spectral components of GS to be tested may greatly exceed the sample size. To control the significance of the result, the detection procedure is implemented by multi-stage testing. In each stage, a Bayesian score test, which introduces a prior to the spectral components, is adopted to face the high dimensional challenge. By setting different priors in each stage, we make the test more powerful against alternatives that have similar bandwidth to the null hypothesis. We prove that the Bayesian score test is locally most powerful in expectation against the alternatives following the given prior. Finally, numerical analysis shows that our method has a good performance in bandwidth detection and is robust to the noise.
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33

Zhu, Yanmin, Tianhao Peng, and Shuzhi Su. "Exponential Multi-Modal Discriminant Feature Fusion for Small Sample Size." IEEE Access 10 (2022): 14507–17. http://dx.doi.org/10.1109/access.2022.3147858.

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34

Kumar, Nirmal, S. K. Singh, V. N. Mishra, G. P. Obi Reddy, and R. K. Bajpai. "Soil quality ranking of a small sample size using AHP." Journal of Soil and Water Conservation 16, no. 4 (2017): 339. http://dx.doi.org/10.5958/2455-7145.2017.00050.9.

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35

Gao, Lin, Danxuan Liu, and Yingli Liu. "Study on Sample Size of Small Batch Precast Concrete Components." IOP Conference Series: Earth and Environmental Science 189 (November 6, 2018): 032024. http://dx.doi.org/10.1088/1755-1315/189/3/032024.

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36

Chafaï, Djalil, and Didier Concordet. "Confidence Regions for the Multinomial Parameter With Small Sample Size." Journal of the American Statistical Association 104, no. 487 (2009): 1071–79. http://dx.doi.org/10.1198/jasa.2009.tm08152.

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37

Mitchell, M. R., R. E. Link, Ge Leyi, and Wang Zhongyu. "Novel Uncertainty-Evaluation Method of Virtual Instrument Small Sample Size." Journal of Testing and Evaluation 36, no. 3 (2008): 101454. http://dx.doi.org/10.1520/jte101454.

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38

Tan, Ming, Hong-Bin Fang, Guo-Liang Tian, and Gang Wei. "Testing multivariate normality in incomplete data of small sample size." Journal of Multivariate Analysis 93, no. 1 (2005): 164–79. http://dx.doi.org/10.1016/j.jmva.2004.02.014.

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39

Feldmann, Rodney M. "Decapod Crustacean Paleobiogeography: Resolving the Problem of Small Sample Size." Short Courses in Paleontology 3 (1990): 303–15. http://dx.doi.org/10.1017/s2475263000001847.

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Studies of paleobiogeography have changed markedly in recent decades transforming a once static subject into one which now has great potential as a useful counterpart to systematic and ecological studies in the interpretation of the geological history of organisms. This has resulted, in large part, from the emergence of plate tectonic models which, in turn, have been used as the bases for extremely sophisticated paleoclimatic modeling. As a result, paleobiogeography has attained a level of precision comparable to that of the studies of paleoecology and systematic paleontology. It is now possible to consider causes for global patterns of origin and dispersal of organisms on a much more realistic level than was previously possible.
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40

Das, Koel, and Zoran Nenadic. "An efficient discriminant-based solution for small sample size problem." Pattern Recognition 42, no. 5 (2009): 857–66. http://dx.doi.org/10.1016/j.patcog.2008.08.036.

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41

Zhang, Li, Wei Da Zhou, and Pei-Chann Chang. "Generalized nonlinear discriminant analysis and its small sample size problems." Neurocomputing 74, no. 4 (2011): 568–74. http://dx.doi.org/10.1016/j.neucom.2010.09.022.

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42

Shriner, J. F., and G. E. Mitchell. "Small sample size effects in statistical analyses of eigenvalue distributions." Zeitschrift für Physik A Hadrons and Nuclei 342, no. 1 (1992): 53–60. http://dx.doi.org/10.1007/bf01294488.

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43

Noble, Robert B., A. John Bailer, Suzanne R. Kunkel, and Jane K. Straker. "Sample size requirements for studying small populations in gerontology research." Health Services and Outcomes Research Methodology 6, no. 1-2 (2006): 59–67. http://dx.doi.org/10.1007/s10742-006-0001-4.

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44

Ma, Xiao-Bing, Feng-Chun Lin, and Yu Zhao. "An Adjustment to the Bartlett's Test for Small Sample Size." Communications in Statistics - Simulation and Computation 44, no. 1 (2014): 257–69. http://dx.doi.org/10.1080/03610918.2013.773347.

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45

Novikov, An A., and I. N. Volodin. "Asymptotics of the necessary sample size under small error probabilities." Journal of Mathematical Sciences 84, no. 3 (1997): 1145–50. http://dx.doi.org/10.1007/bf02398427.

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46

Sivasamy, Shyam. "Sample size considerations in research." Endodontology 35, no. 4 (2023): 304–8. http://dx.doi.org/10.4103/endo.endo_235_23.

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ABSTRACT “What should be the sample size for my study?” is a common question in the minds of every research at some point of the research cycle. Answering this question with confident is tough even for a seasoned researcher. Sample size determination, an important aspect of sampling design consideration of a study, is a factor which directly influences the internal and external validity of the study. Unless the sample size is of adequate size, the results of the study cannot be justified. Conducting a study in too small sample size or too large sample size have ethical, scientific, practical, and economic strings attached to it and have detrimental effects in the research outcomes. A myriad of factors including the study design, type of power analysis, sampling technique employed, and acceptable limits of error fixed play a decisive role in estimating the sample size. However, the advent of free to use software and websites for sample size estimation has actually diluted or sometimes complicated the whole process of sample size estimation as important factors or assumptions related to sample size are overlooked. Engaging a professional biostatistician from the very beginning of the research process would be a wise decision while conducting research. This article highlights the important concepts related to sample size estimation with emphasis on factors which influences it.
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47

Rogers, Gordon, Martin Szomszor, and Jonathan Adams. "Sample size in bibliometric analysis." Scientometrics 125, no. 1 (2020): 777–94. http://dx.doi.org/10.1007/s11192-020-03647-7.

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Abstract While bibliometric analysis is normally able to rely on complete publication sets this is not universally the case. For example, Australia (in ERA) and the UK (in the RAE/REF) use institutional research assessment that may rely on small or fractional parts of researcher output. Using the Category Normalised Citation Impact (CNCI) for the publications of ten universities with similar output (21,000–28,000 articles and reviews) indexed in the Web of Science for 2014–2018, we explore the extent to which a ‘sample’ of institutional data can accurately represent the averages and/or the correct relative status of the population CNCIs. Starting with full institutional data, we find a high variance in average CNCI across 10,000 institutional samples of fewer than 200 papers, which we suggest may be an analytical minimum although smaller samples may be acceptable for qualitative review. When considering the ‘top’ CNCI paper in researcher sets represented by DAIS-ID clusters, we find that samples of 1000 papers provide a good guide to relative (but not absolute) institutional citation performance, which is driven by the abundance of high performing individuals. However, such samples may be perturbed by scarce ‘highly cited’ papers in smaller or less research-intensive units. We draw attention to the significance of this for assessment processes and the further evidence that university rankings are innately unstable and generally unreliable.
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Zheng, Chengyong, Ningning Wang, and Jing Cui. "Hyperspectral Image Classification With Small Training Sample Size Using Superpixel-Guided Training Sample Enlargement." IEEE Transactions on Geoscience and Remote Sensing 57, no. 10 (2019): 7307–16. http://dx.doi.org/10.1109/tgrs.2019.2912330.

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49

Manatunga, Amita K., and Shande Chen. "Sample Size Estimation for Survival Outcomes in Cluster-Randomized Studies with Small Cluster Sizes." Biometrics 56, no. 2 (2000): 616–21. http://dx.doi.org/10.1111/j.0006-341x.2000.00616.x.

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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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Abstract:
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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