Traditional Power Analysis Research Articles

Sampling for biological indicators of soil health: How does sampling methodology affect research results?

A wide variety of soil sampling methods have been adopted, yet limited research has addressed how sampling methods can impact research results particularly for spatially variable soil properties. To address these concerns, we intensively sampled two existing soil health trials in Texas for analysis of soil extracellular enzymatic activities (EEAs), permanganate oxidizable C (POXC), soil organic C (SOC), and pH. The data generated was used to quantify the effect of composite sampling, sampling position (in-row vs between-row), and number of samples on statistical results. We compared a Monte Carlo simulation approach with a traditional power analysis to quantify the impact of the number of samples on our results. Calculated composite sampling resulted in lower statistically significant results compared to discrete sampling, demonstrating lower sensitivity of composite sampling. Sampling in-row resulted in greater values specifically for EEAs, pH, POXC, and SOC. Simulation generated estimated number of samples were mainly driven by the effect size and spatial variation. EEAs were highly variable, resulting in a higher estimated number of samples as compared to other soil properties such as POXC and SOC. In our study with two sites, soil with greater clay content resulted in overall lower estimated number of samples compared to a relatively sandy soil, demonstrating a greater sampling requirement for sandy soil. The simulation approach had a weak correlation with traditional power analysis. The simulation predicted lower estimated number of samples when compared to power analysis, which can help save cost in the long run. Overall, factors like composite sampling, sampling in-row vs between-row, and number of samples taken did impact research results. The impact of these sampling strategies on soil parameters of interest needs to be investigated to achieve the most accurate research results in soil science.

Power Analysis for High Dimensional Neuroimaging Studies

This article considers the problem of sample size determination for power analysis of neuroimaging studies involving multiple comparisons. High dimensional neuroimaging data arise naturally in the context of developing biomarkers for patients with neurological disorders. The standard methodology for power analysis cannot be used or extended in such cases because multiple comparisons involving high dimensional data are more complex and require more parametric information, rather than simply providing the effect size that often plays the vital role in a traditional power analysis problem. This article introduces concepts such as varying effect size and discrimination of null and non-null distributions, while exploring the complexity of relevant data. It systematically develops a sample size determination approach that has a simple interpretation and satisfies some desirable intuitive properties (eg, larger effect size requires a smaller sample, larger sample provides better power). Results are illustrated with a real data set. [ Psychiatr Ann . 2023;53(3):137–144.]

Normalized Power Variance: A new Field Orthogonal to Power in EEG Analysis.

To date, electroencephalogram (EEG) has been used in the diagnosis of epilepsy, dementia, and disturbance of consciousness via the inspection of EEG waves and identification of abnormal electrical discharges and slowing of basic waves. In addition, EEG power analysis combined with a source estimation method like exact-low-resolution-brain-electromagnetic-tomography (eLORETA), which calculates the power of cortical electrical activity from EEG data, has been widely used to investigate cortical electrical activity in neuropsychiatric diseases. However, the recently developed field of mathematics "information geometry" indicates that EEG has another dimension orthogonal to power dimension - that of normalized power variance (NPV). In addition, by introducing the idea of information geometry, a significantly faster convergent estimator of NPV was obtained. Research into this NPV coordinate has been limited thus far. In this study, we applied this NPV analysis of eLORETA to idiopathic normal pressure hydrocephalus (iNPH) patients prior to a cerebrospinal fluid (CSF) shunt operation, where traditional power analysis could not detect any difference associated with CSF shunt operation outcome. Our NPV analysis of eLORETA detected significantly higher NPV values at the high convexity area in the beta frequency band between 17 shunt responders and 19 non-responders. Considering our present and past research findings about NPV, we also discuss the advantage of this application of NPV representing a sensitive early warning signal of cortical impairment. Overall, our findings demonstrated that EEG has another dimension - that of NPV, which contains a lot of information about cortical electrical activity that can be useful in clinical practice.

Sample size calculation for cluster randomized trials with zero-inflated count outcomes.

Cluster randomized trials (CRT) have been widely employed in medical and public health research. Many clinical count outcomes, such as the number of falls in nursing homes, exhibit excessive zero values. In the presence of zero inflation, traditional power analysis methods for count data based on Poisson or negative binomial distribution may be inadequate. In this study, we present a sample size method for CRTs with zero-inflated count outcomes. It is developed based on GEE regression directly modeling the marginal mean of a zero-inflated Poisson outcome, which avoids the challenge of testing two intervention effects under traditional modeling approaches. A closed-form sample size formula is derived which properly accounts for zero inflation, ICCs due to clustering, unbalanced randomization, and variability in cluster size. Robust approaches, including t-distribution-based approximation and Jackknife re-sampling variance estimator, are employed to enhance trial properties under small sample sizes. Extensive simulations are conducted to evaluate the performance of the proposed method. An application example is presented in a real clinical trial setting.

Applications of machine learning techniques in side-channel attacks: a survey

With increasing expansion of the Internet of Things, embedded devices equipped with cryptographic modules become an important factor to protect sensitive data. Even though the employed algorithms in such devices are mathematically secure in theory, adversaries may still be able to compromise them by means of side-channel attacks. In power-based side-channel attacks, the instantaneous power consumption of the target is analyzed with statistical tools to draw conclusions about the secret keys that are used. There is a recent line of work that additionally makes use of techniques from the machine learning domain to attack cryptographic implementations. Since a complete review of this emerging field has not been done so far, this research aims to survey the current state of the art. We use a target-based classification to differentiate published work and drive general conclusions according to a common machine learning workflow. Furthermore, we outline the relationship between traditional power analysis techniques and machine learning-based attacks. This enables researchers to gain a better understanding of the topic in order to design new attack methods as well as potential countermeasures.

Assurance in Intervention Research: A Bayesian Perspective on Statistical Power

AbstractObjective: This article introduces Bayesian assurance as an alternative to traditional power analysis in intervention research. Bayesian assurance is defined as the unconditional probabilit...

Understanding Sample Size Determination in Nursing Research

Sample size determination is a natural consideration in the planning stages of most research studies. The purpose of this work is to provide an overview of modern methods of sample size determination and describe some of the challenges to be aware of and pitfalls to avoid in applying these various methods. Although sample size can be determined in many ways, the most commonly used approach has relied on classical hypothesis-based methods of power analysis. Several challenges often arise with traditional power analysis. For example, the statistical methods used in a power analysis may not adequately address the complexities of the data collected in a research study. The American Psychological Association currently accepts and endorses the use of two methods, power analysis and precision estimation, for sample size determination. Precision estimation is a viable alternative to the traditional power analysis approach. Recommendations for deciding on an approach are provided.

Leakage Power Analysis Attacks: A Novel Class of Attacks to Nanometer Cryptographic Circuits

In this paper, a novel class of power analysis attacks to cryptographic circuits is presented. These attacks aim at recovering the secret key of a cryptographic core from measurements of its static (leakage) power. These attacks exploit the dependence of the leakage current of CMOS integrated circuits on their inputs (including the secret key of the cryptographic algorithm that they implement), as opposite to traditional power analysis attacks that are focused on the dynamic power. For this reason, this novel class of attacks is named ¿leakage power analysis¿ (LPA). Since the leakage power increases much faster than the dynamic power at each new technology generation, LPA attacks are a serious threat to the information security of cryptographic circuits in sub-100-nm technologies. For the first time in the literature, a well-defined procedure to perform LPA attacks that is based on a solid theoretical background is presented. Advantages and measurement issues are also analyzed in comparison with traditional power analysis attacks based on dynamic power measurements. Examples are provided for various circuits, and an experimental attack to a register is performed for the first time. An analytical model of the LPA attack result is also provided to better understand the effectiveness of this technique. The impact of technology scaling is explicitly addressed by means of a simple analytical model and Monte Carlo simulations. Simulations on a 65- and 90-nm technology and experimental results are presented to justify the assumptions and validate the leakage power models that are adopted.

`The International' as Governmentality

In traditional power analysis, `the international' is a characteristic of the states system — an anarchic realm, qualitatively different from the domestic. To traditional norms analysis, the international is increasingly a realm of shared value allocation, akin to other political realms. Given this bifurcation in the literature, privileging power incurs the cost of not being able to study systemic change of the international, whereas privileging norms incurs the cost of not being able to study power. We argue that extant conceptualisations of the international hail from Weber via Morgenthau, for whom international politics was an ideal type applied to the realm between states. Building on Mike Williams's work, we perform a new reading of these two scholars. We find that Morgenthau's identification of the political as an ideal-typical sphere has room for social theoretical insights as found in constructivist theory. Indeed, by his own Weberian lights, Morgenthau's specific ideal type of international politics is in need of updating. We try to rise to the challenge by drawing on Michel Foucault's work in order to forge an understanding of the international as governmentality. The result is a conceptualisation of the international as a socially embedded realm of governmentality. It is a structure (defined by relations of power) that generates different and changing practices of political rule (defined as governmental rationality) and agencies (for example, polities).

Power and sample size estimation in high dimensional biology

Genomic scientists often test thousands of hypotheses in a single experiment. One example is a microarray experiment that seeks to determine differential gene expression among experimental groups. Planning such experiments involves a determination of sample size that will allow meaningful interpretations. Traditional power analysis methods may not be well suited to this task when thousands of hypotheses are tested in a discovery oriented basic research. We introduce the concept of expected discovery rate (EDR) and an approach that combines parametric mixture modelling with parametric bootstrapping to estimate the sample size needed for a desired accuracy of results. While the examples included are derived from microarray studies, the methods, herein, are ‘extraparadigmatic’ in the approach to study design and are applicable to most high dimensional biological situations. Pilot data from three different microarray experiments are used to extrapolate EDR as well as the related false discovery rate at different sample sizes and thresholds.