Proper Use Of Statistics Research Articles

Why is Getting Rid of P-Values So Hard? Musings on Science and Statistics

ABSTRACT The current concerns about reproducibility have focused attention on proper use of statistics across the sciences. This gives statisticians an extraordinary opportunity to change what are widely regarded as statistical practices detrimental to the cause of good science. However, how that should be done is enormously complex, made more difficult by the balkanization of research methods and statistical traditions across scientific subdisciplines. Working within those sciences while also allying with science reform movements—operating simultaneously on the micro and macro levels—are the key to making lasting change in applied science.

Ambigüedades en términos científicos: El uso del "error" y el "sesgo" en estadística

The proper use of statistics is key for professionals who answer questions from data, including ecologists. However, statistics is generally confusing for these professionals, in part due to difficulties related to its terminology. Many of these difficulties derive from the multiple meanings that a term has, both inside and outside the statistical scope. For Spanish-speaking professionals, the translation of English terms also contributes to this confusion. In this paper we show (and intend to clarify) some of these problems from two key terms of an introductory statistics course: error and bias. These terms are discussed in the different contexts that involve problem resolution using statistics: sampling, measurement, estimation, inference and prediction. Error is inherent to statistics and is used to quantify different types of variability or to indicate the possibility of making mistakes on decision making, depending on the context. On the other hand, bias reflects the tendency towards certain values and/or elements, and leads to erroneous conclusions if not avoided. We propose that the problems associated with lexical ambiguity should to be addressed from university teaching and based on this, we offer some recommendations. Thus, the present article not only offers a guide for professionals to make an adequate use of some statistical terms but also provides a contribution for teaching.https://doi.org/10.25260/EA.18.28.3.0.680

Trends in statistical methods in articles published in Archives of Plastic Surgery between 2012 and 2017.

This review article presents an assessment of trends in statistical methods and an evaluation of their appropriateness in articles published in the Archives of Plastic Surgery (APS) from 2012 to 2017. We reviewed 388 original articles published in APS between 2012 and 2017. We categorized the articles that used statistical methods according to the type of statistical method, the number of statistical methods, and the type of statistical software used. We checked whether there were errors in the description of statistical methods and results. A total of 230 articles (59.3%) published in APS between 2012 and 2017 used one or more statistical method. Within these articles, there were 261 applications of statistical methods with continuous or ordinal outcomes, and 139 applications of statistical methods with categorical outcome. The Pearson chi-square test (17.4%) and the Mann-Whitney U test (14.4%) were the most frequently used methods. Errors in describing statistical methods and results were found in 133 of the 230 articles (57.8%). Inadequate description of P-values was the most common error (39.1%). Among the 230 articles that used statistical methods, 71.7% provided details about the statistical software programs used for the analyses. SPSS was predominantly used in the articles that presented statistical analyses. We found that the use of statistical methods in APS has increased over the last 6 years. It seems that researchers have been paying more attention to the proper use of statistics in recent years. It is expected that these positive trends will continue in APS.

Stanley Gregory

Stanley Gregory

On the proper use of statistics

On the proper use of statistics

Lessons for the “Devilish Statistical Obfuscator,”orHow to Argue for a Null Hypothesis: A Guide for Students, Attorneys, and other Professionals

Students, lawyers, and other professionals are sometimes unfamiliar with scientific methodology or the proper use of statistics. Even so, it is likely that they will confront attempts to use social science to prove a null hypothesis, when the data might suggest otherwise if properly handled. As a didactic device, following Erasmus and C. S. Lewis, advice is provided on how to misuse social science for one's own purposes, in hopes that future scholars will not be misled by such inappropriate, though not infrequent, practices. Read More: http://www.amsciepub.com/doi/abs/10.2466/03.11.17.IT.1.2

Statistical Considerations in Medical Journals

The proper use of statistics in medical studies is very important to draw an objective conclusion. The misuse and misunderstanding of statistics degrade the quality of scientific results. Authors should make an effort to choose proper statistical procedures and report the results appropriately. In this article, we review and summarize aspects of statistical consideration in preparing for publication in medical journals.

A conversation with I. Richard Savage (with the assistance of Bruce Spencer)

I. Richard Savage was born in Detroit on October 26, 1925. He attended Northern High School in Detroit and received a bachelor's degree in mathematics at the University of Chicago when he was nineteen. Subsequently, he received a master's degree in mathematics from the University of Michigan (1945) and a Ph.D. in mathematical statistics from Columbia in 1953. While at Columbia, he met JoAnn Osherow and they were married in 1950. They have two daughters, Martha, born in 1951, and Donna, born in 1953. His career began with a three­year (1951–1954) stint as a mathematical statistician in the National Bureau of Standards, followed by a three­ year visiting appointment in the Department of Statistics at Stanford University. From 1957 to 1963, Savage was a professor of Statistics, Biostatistics and Economics at the University of Minnesota. He then spent the next eleven years on the faculty of Florida State University in the Department of Statistics. From 1974 until his retirement in 1990, he was a professor in the Department of Statistics at Yale University. His research interests include rank order statistics, statistics and pub­ lic policy and Bayesian statistics. He served as Coeditor of the Journal of the American Statistical Association (1968, as Editor of the Annals of Statistics (1974–1977), and as President of the American Statistical Association (1984). Because of his commitment to advance proper use of statistics to shape public policy, he has been heavily involved with the Committee on National Statistics of the National Research Council. The book Statistics and Public Policy, edited by Bruce Spencer, was published in 1997 in honor of Savage's contributions to statistics.

Proper and effective training of a pattern recognizer for cyclic data

A new approach to training backpropagation neural networks for identifying cyclic patterns on control charts is presented. The objectives of this research are to show that building an effective cyclic-pattern-recognition neural network requires proper training strategies and to demonstrate how these strategies, namely, incremental and decremental training, should be applied and how the performance can be improved with additional statistics. A series of experiments were conducted to study the effect of the number of output pattern classes and the effect of noise on network training and performance. Experiments show that reducing the number of output pattern classes to a small number, e.g., four or fewer, does not guarantee effective learning, and that the noise added to the training data should be maintained at a reasonable level to achieve a balanced performance. Further incorporation of harmonic amplitude statistics (HAS) also proved that the proper use of statistics adopted from Fourier analysis can improve the performance of a cyclic-pattern-recognition neural network. This study offers valuable insights as to how to construct and train a back-propagation neural network properly and effectively for detecting cyclic patterns.

How applied research can save sociologists from themselves

In this paper I argue that the selection of appropriate statistical procedures is at best haphazard without a careful consideration of the consequences of one’s research, and that significant distortions often will result when applied concerns are ignored. I advance the hypothesis that typical conceptions of basic research in sociology preclude the proper use of statistics. From this it follows that mainstream, quantitative sociology can begin to save itself only by taking seriously the question of applications.

Statistics in information retrieval experiments

Nearly all people use statistics, but very often in the wrong way. To give some clues for the proper use of statistics, a framework will be developed in this paper to help one understand the methodology of applying statistics in IR experiments. The central idea of this framework is that one should i) distinguish between two kinds of models: an expert model and a mathematical-statistical model, and ii) recognize that these two models are highly interdependent. The argument for the need for these two models (and the distinction between them) will follow a meta-scientiftc approach of J. D. Sneed [1]. To make the numerous relationships in this framework more comprehensible a graphical method called Isac is used.

Use of statistics when examining lifetime studies in rodents to detect carcinogenicity.

The lifetime assay for carcinogenicity that subjects groups of 50 animals per sex per dose to three doses and a control is examined for its statistical properties. Using the standard formulation of tests of hypothesis, it is shown that there is a 20-50% chance of having a false positive and that it is possible to define a "weak carcinogen" in terms of the degree of effect that would produce a false negative less than 5% of the time. Whether hypothesis testing is a proper use of statistics in this context is questioned, and alternatives are proposed.

Some Misuses of Health Statistics

Statistics show that the risk of dying from an illness while in the hospital is many times greater than the risk of dying from an illness while at home. By the same token, there is a strong association between dying and being in bed. While most of us may not be able to precisely identify and explain the logical fallacies implied by this type of statistical reasoning, the nonsense of it is sufficiently apparent so that no one is likely to avoid beds or hospitals in order to prolong his life. Yet the same sort of statistics too often finds its way into the general health literature and mass media where the errors of logic are less apparent and pass unrecognized. No one would deny the important contributions that the proper use of statistics has made to the advancement of medicine and public health. Epidemics are brought under control, drugs