• Restorative Dentistry and Endodontics
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• v.49 no.2
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• pp.21.1-21.8
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• 2024

Objectives: This paper aims to serve as a useful guide for sample size determination for various correlation analyses that are based on effect sizes and confidence interval width. Materials and Methods: Sample size determinations are calculated for Pearson's correlation, Spearman's rank correlation, and Kendall's Tau-b correlation. Examples of sample size statements and their justification are also included. Results: Using the same effect sizes, there are differences between the sample size determination of the 3 statistical tests. Based on an empirical calculation, a minimum sample size of 149 is usually adequate for performing both parametric and non-parametric correlation analysis to determine at least a moderate to an excellent degree of correlation with acceptable confidence interval width. Conclusions: Determining data assumption(s) is one of the challenges to offering a valid technique to estimate the required sample size for correlation analyses. Sample size tables are provided and these will help researchers to estimate a minimum sample size requirement based on correlation analyses.

• Journal of Veterinary Clinics
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• v.29 no.1
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• pp.68-77
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• 2012

Whenever planning a study design or preparing a research proposal it is highly recommended that investigators decide the optimum sample size that is required to yield an outcome of interest with a predetermined level of precision. This is because that, all else being equal, if a study with less than the optimum sample size would not detect the significance of differences in reality, and similarly, if a study with more than the optimum sample size will be costly. For these reasons, the majority of peer reviewed biomedical journals assess the adequacy of sample size requirements. The calculated sample size is used as a target number of samples to be collected to provide an estimate of the parameter with the desired and predetermined level of accuracy, and the sample size is a major determinant of the probability of detecting diseased animals from the population. There is no single method of calculating sample size for any given study design. In this context, the purpose of this article is to provide a collection of formulas and examples for some typical situations likely to be encountered in veterinary clinical practice and to highlight the importance of performing prospective sample size calculations when planning a research. Specifically, this paper is concerned with the basic principle of sample size calculation, and considerations for methodological applications were illustrated for a given data set. Also included in this paper is factors influencing sample size calculations using a statistically valid techniques. Appropriate methods to consider these factors are presented.

• Journal of Veterinary Clinics
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• v.32 no.1
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• pp.73-77
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• 2015

There has been increasing attention on sample size requirements in peer reviewed medical literatures. Accordingly, a statistically-valid sample size determination has been described for a variety of medical situations including diagnostic test accuracy studies. If the sample is too small, the estimate is too inaccurate to be useful. On the other hand, a very large sample size would yield the estimate with more accurate than required but may be costly and inefficient. Choosing the optimal sample size depends on statistical considerations, such as the desired precision, statistical power, confidence level and prevalence of disease, and non-statistical considerations, such as resources, cost and sample availability. In a previous paper (J Vet Clin 2012; 29: 68-77) we briefly described the statistical theory behind sample size calculations and provided practical methods of calculating sample size in different situations for different research purposes. This review describes how to calculate sample sizes when assessing diagnostic test performance such as sensitivity and specificity alone. Also included in this paper are tables and formulae to help researchers for designing diagnostic test studies and calculating sample size in studies evaluating test performance. For complex studies clinicians are encouraged to consult a statistician to help in the design and analysis for an accurate determination of the sample size.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.411-418
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• 2007

Sample size calculation is very important in clinical trials. In this paper, we propose sample size calculation method for non-inferiority trials using sample size calculation method suggested by Wang et al.(2003) based on Wilcoxon's rank sum test. Also, sample size comparison between parametric method and proposed method are presented.

• The Journal of the Korean dental association
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• v.54 no.8
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• pp.630-638
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• 2016

The appropriate sample size calculation in dental research is important to achieve the study purpose at the first step in study design. However, it cannot be easy to calculate sample size using standard formulas, because the several factors must be considered for calculation. This study introduced the graphic method for sample size calculation, which is called nomogram. The purpose of this study is to show the effectiveness of the nomogram using examples, expecting the researchers can easily use nomogram for sample size determination.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.2139-2151
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• 2014

Embedded system testing, especially long-term reliability testing, of flash memory solutions such as embedded multi-media card, secure digital card and solid-state drive involves strategic decision making related to test sample size to achieve high test coverage. The test sample size is the number of flash memory devices used in a test. Earlier, there were physical limitations on the testing period and the number of test devices that could be used. Hence, decisions regarding the sample size depended on the experience of human testers owing to the absence of well-defined standards. Moreover, a lack of understanding of the importance of the sample size resulted in field defects due to unexpected user scenarios. In worst cases, users finally detected these defects after several years. In this paper, we propose that a large number of potential field defects can be detected if an adequately large test sample size is used to target weak features during long-term reliability testing of flash memory solutions. In general, a larger test sample size yields better results. However, owing to the limited availability of physical resources, there is a limit on the test sample size that can be used. In this paper, we address this problem by proposing a self-adaptive reliability testing scheme to decide the sample size for effective long-term reliability testing.

• Journal of Veterinary Clinics
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• v.31 no.4
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• pp.288-292
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• 2014

A critical assumption of the standard sample size calculation is that the response (outcome) for an individual patient is completely independent to that for any other patient. However, this assumption no longer holds when there is a lack of statistical independence across subjects seen in cluster randomized designs. In this setting, patients within a cluster are more likely to respond in a similar manner; patient outcomes may correlate strongly within clusters. Thus, direct use of standard sample size formulae for cluster design, ignoring the clustering effect, may result in sample size that are too small, resulting in a study that is under-powered for detecting the desired level of difference between groups. This paper revisit worked examples for sample size calculation provided in a previous paper using nomogram to easy to access. Then we present the concept of cluster design illustrated with worked examples, and introduce design effect that is a factor to inflate the standard sample size estimates.

• Communications for Statistical Applications and Methods
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• v.22 no.4
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• pp.389-399
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• 2015

It is important not to overcalculate sample sizes for clinical trials due to economic, ethical, and scientific reasons. Kang and Kim (2014) investigated the accuracy of a well-known sample size calculation formula based on the approximate power for continuous endpoints in equivalence trials, which has been widely used for Development of Biosimilar Products. They concluded that this formula is overly conservative and that sample size should be calculated based on an exact power. This paper extends these results to binary endpoints for three popular metrics: the risk difference, the log of the relative risk, and the log of the odds ratio. We conclude that the sample size formulae based on the approximate power for binary endpoints in equivalence trials are overly conservative. In many cases, sample sizes to achieve 80% power based on approximate powers have 90% exact power. We propose that sample size should be computed numerically based on the exact power.

• The Korean Journal of Ecology
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• v.21 no.5_1
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• pp.409-418
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• 1998

A molel study was conducted to determine the optimal sample size for the analysis of the aquatic insect community in a stream reach of the Pangtae Creek, Kangwon-do in October 1995 and may 1996. The results showed that the required minimum sample size varied and depended on the purposes of the community analysis. Acoording to the Species: Area Curve method, at least 16 Surber samplings ($30{\times}30cm$) were required in a stream reach in each spring and fall survey. The species diversity index did not vary significantly as the sample size increased. Based on the coefficient of variation analysis, the minimum sample sizes of 10 were required in order to compare seasonal differences of the community in the study area. Considering the static community structure of aquatic insects, including both species numbers and individual numbers of aquatic insects, 11 and 7 samplings were optimal sizes for the fall and spring survey, respectively. We concluded that 12 Surber samplings from 3 riffle-pool sequences (4 samplings at each riffle-pool sequence) would be required in a stream reach (length 1 km) to obtain reliable as well as cost efficient data. Our model showed that the optimal sample size should be determined by interactions between minimum sample size, the degree of data reliability, and cost efficiency.

• Women's Health Nursing
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• v.4 no.3
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• pp.375-387
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• 1998

In clinical trials of nursing research, the sample size determination is one of the most important factor. Although sample size must be considered at the design stage, it has been disregarded in most clinical trials. The power analysis is usually performed before study begins to compute sample size and the power can also be calculated at the end of study in order to justify study result. The power analysis is essential especially when the clinical trials can not show significant differences. In this paper, we review the statistical methods for power analysis and sample size formulae in nursing research. Sample size formulae and the corresponding examples are discussed according to the six types of studies ; mean for one sample, proportion for one sample, means in two samples, proportions in two samples, correlation coefficient and ANOVA.