Basic Statistical Concepts Research Articles

The basic statistical concepts and their interrelationships in diagnostic research.

Many medical postgraduate students exhibit a lack of clarity in their understanding of relevant statistical concepts during the conduct of diagnostic studies. This article, grounded in research practice, delves into the role of understanding statistical concepts in diagnostic research. It includes an exploration of sensitivity, specificity, types of statistical errors, and their interrelationships, as well as a discussion on statistical power-an often-overlooked but crucial concept in research. The article elucidates these important concepts with specific examples and illustrations, and addresses an issue of inconsistency related to the receiver operating characteristic curve in research practice. By drawing analogies between basic concepts in diagnostic tests and concepts in statistics, this article helps to enhance researchers' abilities in designing and interpreting clinical diagnostic studies, thereby improving the quality of clinical diagnostic research.

An Introduction to Bayesian Approaches to Trial Design and Statistics for Stroke Researchers.

While the majority of stroke researchers use frequentist statistics to analyze and present their data, Bayesian statistics are becoming more and more prevalent in stroke research. As opposed to frequentist approaches, which are based on the probability that data equal specific values given underlying unknown parameters, Bayesian approaches are based on the probability that parameters equal specific values given observed data and prior beliefs. The Bayesian paradigm allows researchers to update their beliefs with observed data to provide probabilistic interpretations of key parameters, for example, the probability that a treatment is effective. In this review, we outline the basic concepts of Bayesian statistics as they apply to stroke trials, compare them to the frequentist approach using exemplary data from a randomized trial, and explain how a Bayesian analysis is conducted and interpreted.

Navigating test accuracy metrics used in diagnostic evaluation

Purpose of review Accurate diagnosis stands as the cornerstone of effective medical practice, influencing treatment decisions and patient outcomes. Additionally, grasping basic statistical concepts behind diagnostic tests is crucial for clinicians to interpret results accurately and make informed clinical judgments. This article discusses the evolution and application of diagnostic indices, using a clinical example to apply fundamental concepts. Recent findings Advances in our understanding of statistical methods are giving rise to new interpretations of diagnostic indices, altering how we relate them to test performance. These new metrics reveal previously unnoticed details within existing diagnostic measures. Consequently, these developments have been discussed and will help to increase understanding of their interpretation and utility in clinical settings. Summary This article provides a concise overview of the latest updates on diagnostic indices and stresses the importance of an updated understanding of their inter-connectedness and interpretation for optimal application to evidence-based care.

EXAMINATION OF MATHEMATICS TEACHERS' BELIEFS AND ATTITUDES REGARDING TEACHING STATISTICS

The aim of this study was to examine the beliefs and attitudes of mathematics teachers towards statistics education. The sample of the research consisted of 11 mathematics teachers. A semi-structured interview form was used as the data collection tool, and the data was analyzed using the "cut and sort" technique. Based on the analysis, it was concluded that it is critical to consider both student needs and teacher needs to improve statistics education. Therefore, professional development programs should be organized to help teachers understand and teach basic statistical concepts effectively. In this way, teachers can better equip their students with the skills to think statistically and make rational and informed decisions.

Vaccine hesitancy for COVID19: what is the role of statistical literacy?

Vaccination is an important measure used to control the spread of COVID19. The estimation of risk versus benefit of vaccination is based on the understanding of information about the vaccine. Statistics are frequently part of communications about COVID19. Individuals that do not have an adequate foundation of statistical knowledge may not be able to properly assess associated risks and benefits. This study aims to assess the association between statistical literacy and hesitation to receive the COVID19 vaccine. A nationally representative sample of 2,138 adults, recruited through CINT United States, Inc., (Lawrenceville, NJ; http://www.cint.com), completed an internet survey in the summer of 2021. This survey collected demographic measures and information about COVID19 vaccination status. The competency of respondents on various basic statistical concepts was assessed along with the corresponding confidence of respondents in their answers. A multivariable logistic regression model was constructed to assess the relationship between vaccine hesitancy and statistical literacy while controlling for covariates of interest. Statistical literacy was found to have a negligible association with COVID19 vaccine hesitancy (OR 1.01; 95% CI 1.00-1.02). In addition, differences in the proportion receiving the COVID19 vaccine between political affiliations, income levels, race groups, and ethnicities were observed. The statistical knowledge of the general American public is not commensurate with the need to be literate in basic statistical concepts in the data-driven world in which we live. An effective way to stem vaccine hesitancy may rely on increased statistical knowledge to not be biased by preconceived beliefs shaped by misinformation.

Statistical Literacy in Hand and Upper-Extremity Patients

Statistical literacy is the ability of a patient to apply basic statistical concepts to their health care. Understanding statistics is a critical component of shared decision making. The purpose of this investigation was to define levels of statistical literacy in an upper-extremity (UE) patient population. We aimed to determine if patient demographics would be associated with statistical literacy. An electronic survey was administered to a consecutive series of UE patients at a single institution. We recorded baseline demographics, Single Assessment Numeric Evaluation scores, the Berlin Numeracy Test (BNT), and General Health Numeracy Test. We also included a surgical risk question, which asked: "Approximately 3% of patients who get carpal tunnel surgery develop an infection. If 100 patients get this surgery, how many would you expect to develop an infection?" A covariate-controlled adjusted odds ratio reflecting the association between each statistical literacy outcome measure and patient characteristics was reported. A total 254 surveys were administered, 148 of which were completed and included. Fifty percent of respondents had a high-school education or less. For the BNT, 78% scored in the bottom quartile, and 52% incorrectly answered all questions. For the General Health Numeracy Test, 34% answered 0 or 1/6 questions correctly. For the surgical risk question, 24% of respondents answered incorrectly. Respondents who had a college or graduate degree had 2.62 times greater odds (95% confidence interval, 1.09-6.32) of achieving a BNT score in a higher quartile than patients who did not have a college or graduate degree. Overall levels of statistical literacy are low for UE patients. When engaging in management discussions and shared decision making, UE surgeons should assume low levels of statistical literacy. Consideration of alternative formats, such as frequencies, video-based materials, and pictographs, may be warranted when discussing outcomes and risks of surgical procedures.

An introduction to the significance of sample size in particle analyses for nuclear forensics and radiological investigations

Particulate isotopic analysis in nuclear forensics has developed rapidly during the past two decades due to technical advances in determining the isotopic composition of individual particles. This paper introduces basic statistical concepts that can be applied by analysts to understand the importance of statistical adequacy when interpretating particle data. While these basic statistical methods provide a useful point-of-entry to particle data analysis, more sophisticated statistical and modeling approaches are needed to extract maximal information from such datasets in the future.

Teaching Statistical Inference Through a Conceptual Lens: A Spin on Existing Methods with Examples

Using software to teach statistical inference in introductory courses opens the door for methods and practices that are more conceptually appealing to students. With an increasing number of fields requiring competency in statistics including data science, natural and social sciences, public health and more, it is crucial that we as instructors deliver the basic concepts of statistics effectively. In line with guidelines presented in the GAISE College Report, this article demonstrates intuitive approaches to teaching proportion and mean inference that take advantage of statistical software and emphasize conceptual understanding. The article recommends putting aside asymptotic-based methods for proportion inference and using the exact binomial method. Regarding mean inference, we propose a more contextualized and simplified process that uses the distribution of the sample mean directly and avoids standardized statistics such as z or t. In both the proportion and mean inference contexts, we discuss the benefits of the proposed approaches and provide detailed examples that demonstrate the methods using the Rguroo statistical software.

베이지안 통계를 활용한 특수교육 중재전략의 효과성 평가 방안 탐색

It is important to examine the extent which special education interventions are effective in terms of practical significance. However, because, until now, domain statistical practice in the area of special education is frequentist statistics, there are some limitations of investigating practical significance. In this paper, bayesian statistics was considered as an alternative approach for investigating practical significance. Bayesian statistics is a method of statistics inference in which proability is the most important concept. Therefore, it allows us to focus on the importance of practical significance in special education research. This paper provided the basic concepts of the bayesian statistics and then use a practical example on how to evaluate the effectiveness of special education intervention using bayesian statistics. Results of this study support the evidence that bayesian statistics could be one of alternative approaches for evaluating the effects of interventions in the area of special education. Finally, considerations for future research are discussed.

Revealing attitude toward statistics among MA TEFL students: A Systemic Functional Linguistics perspective

Higher education students’ attitudes toward statistics are of great importance as these may affect student performance and achievement in statistics learning. Despite some interest in attitude towards statistics among higher education students in language-related fields and the broader area of tertiary-level education, the different types of existing attitudes toward statistics might remain uncovered. Applying the Attitude system as an analytical framework, this study explores how higher education students use linguistic resources to indicate statistics-related attitude before, during and after learning. The narratives of learning statistics were obtained from a group of MA TEFL students (n=25) who participated in an introductory course on basic statistical concepts and procedures, among others. The study makes visible a great variety and a considerable variation of linguistic means which students use to express statistics-related attitude as Judgement, Appreciation and Affect, emerging over time. As such, this study advances methodological practices and training in the field.

Factors Influencing Students’ Understanding of Basic Statistical Concepts

This study focused on modelling the influence of teaching practices, students’ attitudes towards statistics, learning practices and perceived ability on students’ understanding in basic statistical concepts using Structural Equation Modelling. A total of 416 students from four Malaysian public tertiary institutions made up the sample for this study. The results showed that students’ perceived ability in statistics has a strong influence on students’ understanding of basic statistical concepts (measured by performance) and students’ attitudes toward statistics. Teaching practices also significantly affect students’ learning practices. This study found that learning practices and teaching practices do not affect students’ attitudes toward statistics and do not lead to an increase in students’ understanding of basic statistical concepts as measured through their test performance.

Five Years of Gene Networks Modeling in Single-cell RNA-sequencing Studies: Current Approaches and Outstanding Challenges

Abstract: Single-cell RNA-sequencing (scRNA-seq) is a rapidly growing field in transcriptomics, which generates a tremendous amount of gene expression data at the single-cell level. Improved statistical approaches and tools are required to extract informative knowledge from such data. Gene network modeling and analysis is one such approach for downstream analysis of scRNA-seq data. Therefore, newer and innovative methods have been introduced in the literature. These approaches greatly vary in their utility, basic statistical concepts, models fitted to the data, etc. Therefore, we present a comprehensive overview of the available approaches for gene network modeling and analysis in single-cell studies, along with their limitations. We also classify the approaches based on different statistical principles and present a class-wise review. We discuss the limitations that are specific to each class of approaches and how they are addressed by subsequent classes of methods. We identify several biological and methodological challenges that must be addressed to enable the development of novel and innovative single-cell gene network inference approaches and tools. These new approaches will be able to analyze the singlecell data efficiently and accurately to better understand the biological systems, increasing the specificity, sensitivity, utility, and relevance of single-cell studies. Furthermore, this review will serve as a catalog and provide guidelines to genome researchers and experimental biologists for objectively choosing the better gene network modeling approach.

USAGE OF IT TECHNOLOGIES IN MEDICINE AND GENOMICS

In this article, we will consider what IT technologies are most used in medicine and by genomics methods in particular, also we will take a look at the use of big data in this matter. Additionally, we will learn what a connectome is, analyze 4M and 3V frameworks in genomics. Statistics in medicine is one of the analysis tools experimental data and clinical observations, as well as the language by means of which the obtained mathematical results are reported. However, this is not the only task of statistics in medicine. Mathematical apparatus widely used for diagnostic purposes, solving classification problems and search for new patterns, for setting new scientific hypotheses. The use of statistical programs presupposes knowledge of the basic methods and stages of statistical analysis: their sequence, necessity and sufficiency. In the proposed presentation, the main emphasis is not on detailed presentation of the formulas that make up the statistical methods, and on their essence and application rules. Finally, we talk through genome-wide association studies, methods of statistical processing of medical data and their relevance. In this article, we analyzed the basic concepts of statistics, statistical methods in medicine and data science, considered several areas in which large amounts of data are used that require modern IT technologies, including genomics, genome-wide association studies, visualization and connectome data collection.

Assessing Lower Secondary School Students’ Common Errors in Statistics

Statistical literacy has been emphasised in the school mathematics curriculum, with the growing concern about students’ ability to think critically in solving statistical problem-solving tasks. However, the current studies revealed that secondary school students’ errors mainly involve the problem of basic concepts in statistics, data interpretation, and the selection of an appropriate representation of data. Therefore, this study aimed to analyse the common errors made by students in solving statistics tasks with multi-level complexity. A survey method was applied in this study. The sample of this study consisted of 356 Form One (Grade 7) students from eight secondary schools. The instrument of this study consisted of five superitem tasks, which represented the five content domains: line graph, bar graph, pie chart, dot plot, and histogram. There are four levels of items in each superitem task. Thus, the total number of items is 20. The format of all the 20 items in the five superitem tasks is open-ended. The common errors were then analysed based on all the participants’ solutions shown in their answer script. The findings found that most students could not achieve the highest level of statistical competency. They failed to think qualitatively while justifying data. This study provides a meaningful analysis that assists the teaching and learning of statistics to better link numeracy and literacy. The application of the superitem tasks provides valuable information that enables the teachers to understand their students’ statistical processes better.

Prompted Self-Explanations Improve Learning in Statistics but Not Retention

Background College students often do not retain what they learn in Statistics in order to apply it in Experimental Psychology. Self-explanation, that is, elaborating on what one is trying to learn by asking questions, making inferences, etc., improves learning and may improve retention. Objective The purpose of this study was to determine whether self-explanation was superior to students’ usual study methods specifically for learning some basic concepts in statistics, and, if so, if it was similarly useful for retention a semester after the initial learning. Method We used 199 college students as participants in a randomized, between participant, two-part experiment examining the effects of training by prompting self-explanations as a potential solution to this applied problem. Results The self-explanations that we elicited improved initial learning and were superior to students’ usual study methods, but did not benefit retention. Conclusions Future research on improving the quality of the self-explanations and training with spaced retrieval practice, in order to benefit retention, is suggested. Teaching Implication Self-explanation should be implemented for teaching statistics in order to benefit initial learning. However, teachers should explore other methods to accomplish retention.

The risk of bias in denoising methods: Examples from neuroimaging.

Experimental datasets are growing rapidly in size, scope, and detail, but the value of these datasets is limited by unwanted measurement noise. It is therefore tempting to apply analysis techniques that attempt to reduce noise and enhance signals of interest. In this paper, we draw attention to the possibility that denoising methods may introduce bias and lead to incorrect scientific inferences. To present our case, we first review the basic statistical concepts of bias and variance. Denoising techniques typically reduce variance observed across repeated measurements, but this can come at the expense of introducing bias to the average expected outcome. We then conduct three simple simulations that provide concrete examples of how bias may manifest in everyday situations. These simulations reveal several findings that may be surprising and counterintuitive: (i) different methods can be equally effective at reducing variance but some incur bias while others do not, (ii) identifying methods that better recover ground truth does not guarantee the absence of bias, (iii) bias can arise even if one has specific knowledge of properties of the signal of interest. We suggest that researchers should consider and possibly quantify bias before deploying denoising methods on important research data.

Conscious and Correct Use of Biostatistical Methods in Medical Researches: From Planning to Reporting the Results - Part I

The principles and methods of biostatistics serve as a guide for healthcare practitioners in both their daily and scientific work. Biostatistics principles and methods should be considered at every stage. A scientific research requires a multidisciplinary teamwork and each team must include a biostatistics expert at the planning stage of the study. During the planning phase, a biostatistics expert can answer all questions about the design, conduct, data processing, data analysis, presentation of the results, and publication of the results. Reporting guidelines for almost all research designs were developed to improve the quality and transparency of research reports. When the research results are presented in a structured and standardized way using these guidelines, it will allow the readers to interpret the findings more easily and accurately. It is now easier for researchers to gain access to a variety of software or web applications that allow them to perform basic and advanced statistical analyses. However, erroneous/misleading results are produced when these software are used unconsciously or incorrectly by those with insufficient statistical knowledge. Health professionals should also have sufficient statistical knowledge to identify errors in articles related to their field. Therefore, they are expected to have knowledge of research designs and basic statistical concepts. Knowing the basic concepts such as population, sample, sampling methods, random assignment of volunteers to groups, parameters, statistics, data types, etc. will help the readers while evaluating the material and method sections of the articles.

The Role and Significance of Statistics in Economic and Social Development

With the rapid development and great progress of the social and economic system in China and the world in the new era, the application of statistics has played an increasingly important role in the development of the national economic operation system and economic society. Statistics is an important means of collecting, sorting, analyzing, processing, and demonstrating data related to economic and social development in economic development. The theoretical system and method play a key role in sorting out and analyzing the economic situation, process and trend of the Chinese people and the world. At the same time, it has extensive and far-reaching significance for governments and departments at all levels to scientifically formulate economic development directions, understand the laws of economic and social operation, and grasp the basic state of economic trends. This paper mainly starts from the basic concepts of statistics, combines the current situation of economic development, and explains and discusses the important role and effect of statistics in economic development according to the application and reaction of relevant knowledge of statistics in the current rapid economic development. profound meaning.

Examination of the Relationship between Statistical Literacy Levels and Statistical Literacy Self-Efficacy of High School Students

The purpose of this study was to examine the existence of a relationship between statistical literacy levels and statistical literacy self-efficacy of high school students. A total of 163 high school students studying at two different high schools in the 10 th , 11 th and 12 th grades constituted the study sample. The “Statistical Literacy Self-Efficacy Instrument” and selected questions from the “Levels of Conceptual Understanding in Statistics (LOCUS)” project, adapted into Turkish, were utilized for data collection. The data were analyzed using both qualitative and quantitative methods according to a correlational research design. It was determined from the results that the statistical literacy of high school students was at a “Low” level and their statistical literacy self-efficacy was at an “Intermediate” level. Importantly, the statistical literacy self-efficacy of students was found to be a significant predictor of statistical literacy. It was determined that the strongest predictor of statistical literacy were factors regarding efficacy related to basic statistical concepts and confidence related to the statistical process that follows, while the weakest predictor was a factor regarding the belief related to statistical reasoning. Thus, it was important that this research emphasized the affective aspect of statistical literacy in particular and portrayed that this aspect was of great importance for students’ statistical literacy. As a result, as part of the statistical teaching and learning process, activities aimed at developing the statistical literacy self-efficacy of students parallel to the target of students’ statistical literacy is proposed.

Is glass a state of matter?

Glass is everywhere. We use and are surrounded by glass objects which make tangible the reality of glass as a distinct state of matter. Yet, glass as we know it is usually obtained by cooling a liquid sufficiently rapidly below its melting point to avoid crystallisation. The viscosity of this supercooled liquid increases by many orders of magnitude upon cooling, until the liquid becomes essentially arrested on experimental timescales below the ‘glass transition‘ temperature. From a structural viewpoint, the obtained glass still very much resembles the disordered liquid, but from a mechanical viewpoint, it is as rigid as an ordered crystal. Does glass qualify as a separate state of matter? We provide a pedagogical perspective on this question using basic statistical mechanical concepts. We recall the definitions of states of matter and of phase transitions between them. We review recent theoretical results suggesting why and how an ‘ideal glass’ can indeed be defined as a separate equilibrium state of matter. We discuss recent success of computer simulations trying to analyse this glass state. We close with some experimental perspectives.