Basic Statistical Concepts Research Articles

“StatLit: tactical interventions for strategic transformation”

PurposeThis paper was inspired by an Association of College and Research Libraries Biennial Conference 2017 roundtable that aimed to transform existing notions about statistical literacy in information literacy instruction.Design/methodology/approachIn Baltimore, the authors facilitated an in-person discussion and synchronous virtual dialogue via Twitter and Periscope about teaching basic statistical concepts (such as mean vs median) and the real implications of “statistical significance”.FindingsA recording of the roundtable is available here: http://bit.ly/StatLit.Originality/valueThe goal of the StatLit roundtable was to inspire transformational thinking for teaching basic statistical reasoning to students within a library context.

Alfabetizacion Estadistica en Profesores de Distintos Niveles Formativos

Resumen: El presente artículo tiene por objetivo evaluar los niveles de alfabetización estadística y la percepción que tienen estudiantes en Formación Inicial Docente y profesores en ejercicio del sistema escolar. Para tal efecto, se aplicaron dos instrumentos: el primero, de alfabetización estadística y el segundo de percepción, validados estadística. Los resultados evidencian que, en general, los estudiantes en Formación Inicial Docente y profesores en ejercicio presentan porcentajes de logro descendidos en la descodificación de situaciones problemáticas de tipo textual, entregando además argumentaciones poco plausibles con los saberes estadísticos donde intervienen conceptos básicos de estadística.

Importancia del cálculo de la sensibilidad, la especificidad y otros parámetros estadísticos en el uso de las pruebas de diagnóstico clínico y de laboratorio

La elección de una prueba a realizar para un paciente, así como su interpretación, es un escenario diario al cual el médico se debe enfrentar y para el cual debe aplicar su juicio crítico basado en las evidencias informadas. Es común que cuando se habla de una prueba de diagnóstico clínico o de laboratorio se describan parámetros como la sensibilidad, la especificidad y los valores predictivos positivos y negativos. Estos reflejan las características de una prueba diagnóstica y sirven para decidir en qué momento se deben utilizar (sensibilidad y especificidad de una prueba) o qué significado tiene el resultado de una prueba en un paciente en particular. Cuando se trata de comparar estos parámetros en diferentes pruebas y optar por la que es de mayor utilidad en el diagnóstico de una enfermedad determinada, es indispensable que el médico conozca y aprenda cómo se obtienen estas medidas y cuál es su interpretación para decidir la conducta más apropiada para el paciente. El objetivo de la presente revisión es ofrecer los conceptos estadísticos básicos y simples para la comprensión y aplicación de las pruebas de diagnóstico clínico y de laboratorio.

Statistics for Method Verification of Qualitative Assays in Clinical Microbiology

Despite improvement over the last decade, inadequate method verification strategies and documentation are still common laboratory inspection deficiencies. Some of the gaps occur because microbiologists may not use scientific experiment concepts, such as experimental design, data validation, biostatistics, and electronic documentation, that can improve their verification strategies for new test methods. Other gaps occur because laboratory resources are often limited. Clinical laboratories face tough choices when they consider implementing a new test method, and this review summarizes the basic statistical concepts that microbiologists need to critically plan and review qualitative method verification with the aim of determining whether a new diagnostic test is fit for patient testing.

On the accuracy and theoretical underpinnings of the multiple variance Brownian motion approach for estimating variable rates and inferring ancestral states

In a recent contribution, Griffin & Yapuncich (2016) (‘G&Y’) report on purported inaccuracies and lack of theoretical underpinnings of our recently proposed ancestral estimation procedure. Our method is designed to provide a good overall fit with the data when different branches may be subject to different rates of change (multiple variance Brownian motion, or ‘mvBM’). Here we demonstrate that G&Y’s theoretical concerns stem from a misinformed account of basic statistical concepts and procedures, a misinterpretation of the primary literature and a circular adherence to a restrictive model of evolution (standard Brownian motion, or ‘standard BM’) whose usefulness has long been considered inappropriate for modelling branch-specific evolutionary patterns. We further apply a series of simple tests that falsify G&Y’s claims on every account. Finally, we show that including a range of sample sizes (from 4 through 100) to G&Y’s own suggested simulation procedure further substantiates what they purport to falsify: the validity of mvBM when modelling potential deviations from standard BM in trait evolution.

Formal and Informal Learning and First-Year Psychology Students' Development of Scientific Thinking: A Two-Wave Panel Study.

Scientific thinking is a predicate for scientific inquiry, and thus important to develop early in psychology students as potential future researchers. The present research is aimed at fathoming the contributions of formal and informal learning experiences to psychology students’ development of scientific thinking during their 1st-year of study. We hypothesize that informal experiences are relevant beyond formal experiences. First-year psychology student cohorts from various European countries will be assessed at the beginning and again at the end of the second semester. Assessments of scientific thinking will include scientific reasoning skills, the understanding of basic statistics concepts, and epistemic cognition. Formal learning experiences will include engagement in academic activities which are guided by university authorities. Informal learning experiences will include non-compulsory, self-guided learning experiences. Formal and informal experiences will be assessed with a newly developed survey. As dispositional predictors, students’ need for cognition and self-efficacy in psychological science will be assessed. In a structural equation model, students’ learning experiences and personal dispositions will be examined as predictors of their development of scientific thinking. Commonalities and differences in predictive weights across universities will be tested. The project is aimed at contributing information for designing university environments to optimize the development of students’ scientific thinking.

Research and Teaching: Online Collaborative Misconception Mapping Strategy Enhanced Health Science Students' Discussion and Knowledge of Basic Statistical Concepts

Research and Teaching: Online Collaborative Misconception Mapping Strategy Enhanced Health Science Students' Discussion and Knowledge of Basic Statistical Concepts

Statistics - It's Love or Hate [Associate Editor's View

Discusses the significance and necessity of statistics from an engineering perspective. Somehow,most mathematicians miss the ability to communicate basic statistical concepts to electrical engineering students. The world is still waiting for an intuitive approach to familiarize student engineers with this field. And that is very necessary. The properties of large collections of objects, people, events, and measurements are captured with statistics. When the propulsion and construction of a rocket are known, a meteorite is targeted with unbelievable precision. But throwing a few dice require statistics. Statistics are perhaps the only mathematical tool without a counterpart in reality.

The Impact of an Interactive Statistics Module on Novices' Development of Scientific Process Skills and Attitudes in a First-Semester Research Foundations Course.

Evidence suggests that incorporating quantitative reasoning exercises into existent curricular frameworks within the science, technology, engineering, and mathematics (STEM) disciplines is essential for novices’ development of conceptual understanding and process skills in these domains. Despite this being the case, such studies acknowledge that students often experience difficulty in applying mathematics in the context of scientific problems. To address this concern, the present study sought to explore the impact of active demonstrations and critical reading exercises on novices’ comprehension of basic statistical concepts, including hypothesis testing, experimental design, and interpretation of research findings. Students first engaged in a highly interactive height activity that served to intuitively illustrate normal distribution, mean, standard deviation, and sample selection criteria. To enforce practical applications of standard deviation and p-value, student teams were subsequently assigned a figure from a peer-reviewed primary research article and instructed to evaluate the trustworthiness of the data. At the conclusion of this exercise, students presented their evaluations to the class for open discussion and commentary. Quantitative assessment of pre- and post-module survey data indicated a statistically significant increase both in students’ scientific reasoning and process skills and in their self-reported confidence in understanding the statistical concepts presented in the module. Furthermore, data indicated that the majority of students (>85%) found the module both interesting and helpful in nature. Future studies will seek to develop additional, novel exercises within this area and to evaluate the impact of such modules across a variety of STEM and non-STEM contexts.

Pruebas diagnósticas: aspectos básicos de lo que el endoscopista debe saber

Choosing a test on a patient and its interpretation is a daily occurrence that the specialist is faced with, and the endoscopist is no stranger to this. It is common when talking of a diagnostic test (laboratory or imaging) to hear the mention of parameters such as sensitivity, specificity and predictive values. The parameters mentioned (sensitivity, specificity) establish the characteristics of a diagnostic test and are useful to decide when we should use them or the meaning that a test result has in a particular patient.The aim of the study is to present the basic statistical concepts for the understanding and application of diagnostic tests.

WE‐B‐201‐02: Statistical Failings That Can Keep Us All in the Dark

Evidence is growing to suggest that many published clinical results cannot be replicated. At the same time, the number of published clinical papers is steadily increasing and most if not all base their conclusions on evidence provided by formal statistical tests. Medical physicists have a critical need to understand and be able to interpret the methods and results of these studies in order to judge their scientific quality and relevance. However many medical physicists have minimal or no training in the sort of practical statistical methods commonly found in the literature. They may therefore have an inadequate ability to detect statistical errors and limitations that are an unfortunately too frequent occurrence in clinical papers.In this session we will use published examples to demonstrate common statistical errors frequently encountered in peer‐reviewed literature, distinctive symptoms that can help detect these errors, and explanations for how they might have been corrected. In the process, we will explain some of the basic concepts of inferential statistics. Some specific case studies we will cover will include misunderstanding the meaning of p‐values and clinical significance, misinterpreting statistical power, not accounting for multiple‐hypothesis testing, ignoring missing data, and faulty survival analysis. Learning Objectives: Learn about the presence of statistical problems in published studies Identify common signs and symptoms of potential problems in various types of statistical tests Learn methods for correctly implementing statistical analyses of the type commonly found in clinical publications

WE‐B‐201‐01: Introduction: Not Everything You Read Is True

Evidence is growing to suggest that many published clinical results cannot be replicated. At the same time, the number of published clinical papers is steadily increasing and most if not all base their conclusions on evidence provided by formal statistical tests. Medical physicists have a critical need to understand and be able to interpret the methods and results of these studies in order to judge their scientific quality and relevance. However many medical physicists have minimal or no training in the sort of practical statistical methods commonly found in the literature. They may therefore have an inadequate ability to detect statistical errors and limitations that are an unfortunately too frequent occurrence in clinical papers.In this session we will use published examples to demonstrate common statistical errors frequently encountered in peer‐reviewed literature, distinctive symptoms that can help detect these errors, and explanations for how they might have been corrected. In the process, we will explain some of the basic concepts of inferential statistics. Some specific case studies we will cover will include misunderstanding the meaning of p‐values and clinical significance, misinterpreting statistical power, not accounting for multiple‐hypothesis testing, ignoring missing data, and faulty survival analysis. Learning Objectives: Learn about the presence of statistical problems in published studies Identify common signs and symptoms of potential problems in various types of statistical tests Learn methods for correctly implementing statistical analyses of the type commonly found in clinical publications

WE‐B‐201‐00: Statistical Failings That Keep Us All in the Dark

Evidence is growing to suggest that many published clinical results cannot be replicated. At the same time, the number of published clinical papers is steadily increasing and most if not all base their conclusions on evidence provided by formal statistical tests. Medical physicists have a critical need to understand and be able to interpret the methods and results of these studies in order to judge their scientific quality and relevance. However many medical physicists have minimal or no training in the sort of practical statistical methods commonly found in the literature. They may therefore have an inadequate ability to detect statistical errors and limitations that are an unfortunately too frequent occurrence in clinical papers.In this session we will use published examples to demonstrate common statistical errors frequently encountered in peer‐reviewed literature, distinctive symptoms that can help detect these errors, and explanations for how they might have been corrected. In the process, we will explain some of the basic concepts of inferential statistics. Some specific case studies we will cover will include misunderstanding the meaning of p‐values and clinical significance, misinterpreting statistical power, not accounting for multiple‐hypothesis testing, ignoring missing data, and faulty survival analysis. Learning Objectives: Learn about the presence of statistical problems in published studies Identify common signs and symptoms of potential problems in various types of statistical tests Learn methods for correctly implementing statistical analyses of the type commonly found in clinical publications

Vocabulary of concepts and terms in chemometrics (IUPAC Recommendations 2016)

Abstract Recommendations are given concerning the terminology relating to chemometrics. Building on ISO definitions of terms for basic concepts in statistics the vocabulary is concerned with mainstream chemometric methods. Where methods are used widely in science, definitions are given that are most useful to chemical applications. Vocabularies are given for general data processing, experimental design, classification, calibration and general multivariate methods.

A frequency-domain derivation of shot-noise

A formula for shot-noise is derived in the frequency-domain. The derivation is complete and reasonably rigorous while being appropriate for undergraduate students; it models a sequence of random pulses using Fourier sine and cosine series, and requires some basic statistical concepts. The text here may serve as a pedagogic introduction to the spectral analysis of random processes and may prove useful to introduce students to the logic behind stochastic problems. The concepts of noise power spectral density and equivalent noise bandwidth are introduced.

R을 활용한 '대화형 통계학 입문 실습실' 개발과 활용

본 연구에서는 최근 통계 분야에서 활용도가 급격히 높아지고 있는 중요한 컴퓨터 언어이자 오픈 소스 통계 프로그램인 R을 활용하는 '대화형(interactive) 통계학 입문 실습실'의 개발 과정과 내용 및 활용을 다룬다. 최근에 개발을 마치고 2014 서울 세계수학자대회에서 소개된 후, 통계 강좌 등에 실제 사용되는 웹상의 R을 활용한 본 대화형 통계학 입문 실습실의 특징은 웹상에서 본문과 예제의 설명 및 풀이과정과 함께 대응하는 R 명령어 코드들을 함께 박스안에 제공하여, 실습 때마다 일일이 컴퓨터 명령어 코드들을 입력해야하는 번거로움을 없앴다. 또한, 명령어의 실행을 위하여 프로그램을 설치하지 않고, 명령어 상자 아래 제공한 [클릭-실행] 버튼을 누르기만 하면, 클라우드 컴퓨팅으로 그 결과값과 그래픽을 동시에 바로 같은 화면에서 확인하면서, 시뮬레이션 및 실습을 할 수 있고, 더 나아가 그와 유사한 다른 문제에 함수와 조건만을 수정하여 바로 사용할 수 있는 편리함이 추가되었다. 그 결과 대화형 통계학 입문 실습실에서는 R 명령어를 이해하는 데 필요한 시간과 노력이 대폭 줄어들 뿐 아니라, 초보자에게 통계학 입문 과목을 지도하기에 적절하며, 그밖에 다양한 Java 시각화 도구와 이미지 및 통계 자료를 사용하여 사용자 맞춤형 강의실 개발이 가능하여 통계학입문 강의를 수강하는 학생들의 관심과 흥미를 유도할 수 있도록 하였다. 본 연구에서는 본 실습실을 통계입문 강좌의 효과적인 실습실 모델의 하나로 소개한다. In this paper, we introduce development process and application of a simple and effective model of a statistics laboratory using open source software R, one of leading language and environment for statistical computing and graphics. This model consists of HTML files, including Sage cells, video lectures and enough internet resources. Users do not have to install statistical softwares to run their code. Clicking 'evaluate' button in the web page displays the result that is calculated through cloud-computing environment. Hence, with any type of mobile equipment and internet, learners can freely practice statistical concepts and theorems via various examples with sample R (or Sage) codes which were given, while instructors can easily design and modify it for his/her lectures, only gathering many existing resources and editing HTML file. This will be a resonable model of laboratory for studying statistics. This model with bunch of provided materials will reduce the time and effort needed for R-beginners to be acquainted with and understand R language and also stimulate beginners' interest in statistics. We introduce this interactive statistical laboratory as an useful model for beginners to learn basic statistical concepts and R.

Multivariate assessment of event-related potentials with the t-CWT method.

BackgroundEvent-related brain potentials (ERPs) are usually assessed with univariate statistical tests although they are essentially multivariate objects. Brain–computer interface applications are a notable exception to this practice, because they are based on multivariate classification of single-trial ERPs. Multivariate ERP assessment can be facilitated by feature extraction methods. One such method is t-CWT, a mathematical-statistical algorithm based on the continuous wavelet transform (CWT) and Student’s t-test.ResultsThis article begins with a geometric primer on some basic concepts of multivariate statistics as applied to ERP assessment in general and to the t-CWT method in particular. Further, it presents for the first time a detailed, step-by-step, formal mathematical description of the t-CWT algorithm. A new multivariate outlier rejection procedure based on principal component analysis in the frequency domain is presented as an important pre-processing step. The MATLAB and GNU Octave implementation of t-CWT is also made publicly available for the first time as free and open source code. The method is demonstrated on some example ERP data obtained in a passive oddball paradigm. Finally, some conceptually novel applications of the multivariate approach in general and of the t-CWT method in particular are suggested and discussed.ConclusionsHopefully, the publication of both the t-CWT source code and its underlying mathematical algorithm along with a didactic geometric introduction to some basic concepts of multivariate statistics would make t-CWT more accessible to both users and developers in the field of neuroscience research.Electronic supplementary materialThe online version of this article (doi:10.1186/s12868-015-0185-z) contains supplementary material, which is available to authorized users.

Dynamic-Interactive Graphics for Statistics (26 Years Later)

This paper briefly reviews the history of dynamic-interactive graphics for statistics, introduces an example of such graphics, and provides a few glimpses as to the current state of things and the future trends we envision. The general conclusion is that dynamic-interactive graphics for statistics are thriving more than ever as they shift from the desktop to the internet. Thus, dynamic-interactive graphics are becoming increasingly important as they: 1) provide non-experts in statistics with the means to carry out analyses on their own; and 2) teach the basic concepts of statistics to students and practitioners with low to moderate mathematics skills. Their increasing popularity makes the lessons learned over the past 26 years of research on the subject more important than ever.

O USO DO SPSS (STATISTICAL PACKAGE FOR THE SOCIAL SCIENCES) NA CIÊNCIA POLÍTICA: UMA BREVE INTRODUÇÃO

This paper deals with some basic concepts of statistics a few analysis methods and their usefulness for researchers from various fields. In particular we focus our argument on how SPSS can be used with a view to carrying out a large number of inferences from certain set of quantitative data. Above all, we seek through these basic concepts and handling of said software routines equip, minimally, those who wish to venture quantitative analysis. In particular, we are concerned the use of the Pearson Correlation Test, Chi-Square Test and Analysis of Simple and Multiple Regression.

Hierarchical modeling in spatial epidemiology

This paper considers the basic concepts and methods used in hierarchical modeling for data arising in spatial epidemiology. Following discussion of basic statistical and epidemiological concepts relevant to small‐area health studies, the paper reviews the different approaches to model formulation, parameter estimation, and also software resources. WIREs Comput Stat 2014, 6:405–417. doi: 10.1002/wics.1315This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Applications of Computational Statistics > Health and Medical Data/Informatics Data: Types and Structure > Image and Spatial Data