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Statistical assumptions are the basis of many univariate and multivariate statistical tests. Normality is the most basic assumption of multivariate analysis in plant ecology. If the normality assumption is violated, some specific statistical tests are not valid. Therefore, the present study compares the methods of normality assessment of some soil properties in the arid land of South Khorasan. It also examines the effect of increasing the number of soil samples from 25 to 50 on the normality results. Histogram, box plot, Q-Q plot, CV, skewness, and univariate and multivariate normality tests were used. The results showed that EC, K, Ca, Mg, Na, Cl, HCO3, and SAR data had a very high variation (CV 75–100%) and saturation moisture and pH had a low variation (CV

The constant passage of time has rendered the Intensity-Duration-Frequency (IDF) curve maps for the province of Villa Clara outdated. Specifically, the municipality of Caibarién lacks them, so the ones outlined in the Cuban standard NC 1239:2018, are used to build hydraulic works in the area. A study is conducted with the primary objective of developing IDF curves associated with convective rainfall events in this locality, and proposes a procedure for elaborating such curves. To achieve this, novel techniques of fitting to distribution functions and parametric equations are used. [ABSTRACT FROM AUTHOR]

Online education has been implemented in Turkiye as well as in many countries of the world in recent years and is becoming more widespread day by day. Being able to take place anytime and anywhere, eliminating the limitations of time and space, and providing lifelong learning can be described as the most basic factors in the spread of online education. In addition, due to the rapid increase in the number of learners who have to take online education due to the Covid-19 global epidemic, which has affected the whole world recently, it has become an important issue for learners to acquire knowledge and skills in online distance learning environments. Also, it is necessary to determine the level of developing a sense of community in order to ensure the learners' self-expression in the relevant environments, their active participation and the continuity of the learning action. In this study, it was aimed to determine the level of sense of community of learners studying at Turkish-German University in the 2021-2022 academic year in online distance education environments in terms of different variables like gender, level of education, and their ability to use computer programs and applications. The research was carried out in the cross-sectional scanning model. The obtained data are analyzed with the help of SPSS package program. The findings are evaluated at the 95% confidence interval and at the 5% significance level. Sub-factor averages, factor correlations are examined and diagnostic statistics related to the study group are included. In addition, t-test is used for variables with two sublevels and one-way Anova is used for variables with more than two sublevels. If there is a difference in the Anova test, Post-Hoc tests are used to determine the pairwise differences. As a result, in order to make education more efficient in online learning environments, it is concluded that in order to strengthen the sense of community, learners can be given tasks that they can discuss and negotiate, learners can be made to work in groups in related environments, etc. In adddition, considering the stress, anxiety and uncertainty that learners and educators face with the global epidemic, it is thought that social presence should take priority for learning to take place. It is recommended that the study be developed by applying it to larger regions and samples. [ABSTRACT FROM AUTHOR]

Statistical assumptions are the basis of many univariate and multivariate statistical tests. Normality is the most basic assumption of multivariate analysis in plant ecology. If the normality assumption is violated, some specific statistical tests are not valid. Therefore, the present study compares the methods of normality assessment of some soil properties in the arid land of South Khorasan. It also examines the effect of increasing the number of soil samples from 25 to 50 on the normality results. Histogram, box plot, Q-Q plot, CV, skewness, and univariate and multivariate normality tests were used. The results showed that EC, K, Ca, Mg, Na, Cl, HCO3, and SAR data had a very high variation (CV 75-100%) and saturation moisture and pH had a low variation (CV <15%). Based on the results of most statistical tests and the skewness coefficient, saturation moisture, pH, N, P, CaCO3, sand, and silt were normal. EC, K, Ca, Mg, Na, Cl, HCO3 and SAR had the right skewed distribution. The results showed multivariate normality was violated, and the use of these data was not suitable for multivariate analysis. The results of the goodness-of-fit test showed that P, sand, and silt follow a normal distribution. Other soil properties do not follow any of the studied probability distributions (p≥0.05). Therefore, the use of nonparametric is recommended for the physical and chemical properties of the soil in the area. Although in general, the increase in the number of samples has a positive effect on the actual distribution of the community, but due to the high spatial variability of some soil properties such as salinity, the status of nutrients, particle size, etc., the CV and the range of variations in most of soil properties are wide. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Internet Applications & Management / İnternet Uygulamaları ve Yönetimi Dergisi is the property of Journal of Internet Applications & Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Statistical analysis is an integral part of medical research. It helps transform raw data into meaningful insights, supports hypothesis testing, optimises study design, assesses risk and prognosis, and facilitates evidence-based decision-making. The statistical analysis increases research findings' reliability, validity and generalisability, ultimately advancing medical knowledge and improving patient care. Without it, meaningful analysis of the data collected would be impossible. The conclusions drawn would be unsubstantiated and misleading. Many health professionals are unfamiliar with statistical analysis and its basic concepts. The analysis of clinical data is an integral part of medical research. Identifying the data type (continuous, quasi-continuous or discrete) and detecting outliers are the first and most important steps. When analysing the data distribution for normality, graphical and numerical methods are recommended. Depending on the type of data distribution, appropriate non-parametric or parametric tests can be used for further analysis. Data that are not normally distributed can be normalised using various mathematical methods (e.g., square root or logarithm) and analysed using parametric tests in the next steps. This review provides essential explanations of these concepts without using complex mathematical or statistical equations but with several graphical examples of various statistical terms. [ABSTRACT FROM AUTHOR]

The effects of the EU-UK Withdrawal Agreement, reached on 17 October 2019, on the stocks listed on the London Stock Exchange are the subject of the paper. The classic event study methodology was used to quantify impact. This research is based on a sample of 138 stocks, divided into five sample sections according to the company’s business sector. In contrast to the research conducted after the referendum, which showed a clear negative impact on almost all of the observed sectors, the research conducted in this paper does not provide a unique conclusion. Three sectors recorded obvious positive effects, namely the financial sector, the food industry, and the medical sector, while no sector suffered obvious negative effects. The remaining two sectors did not provide data to aid in reaching a clear conclusion, as there were positive, negative and statistically insignificant results across different tests. [ABSTRACT FROM AUTHOR]

Statistical analysis is an integral part of medical research. It helps transform raw data into meaningful insights, supports hypothesis testing, optimises study design, assesses risk and prognosis, and facilitates evidence-based decision-making. The statistical analysis increases research findings' reliability, validity and generalisability, ultimately advancing medical knowledge and improving patient care. Without it, meaningful analysis of the data collected would be impossible. The conclusions drawn would be unsubstantiated and misleading. Many health professionals are unfamiliar with statistical analysis and its basic concepts. The analysis of clinical data is an integral part of medical research. Identifying the data type (continuous, quasi-continuous or discrete) and detecting outliers are the first and most important steps. When analysing the data distribution for normality, graphical and numerical methods are recommended. Depending on the type of data distribution, appropriate non-parametric or parametric tests can be used for further analysis. Data that are not normally distributed can be normalised using various mathematical methods (e.g., square root or logarithm) and analysed using parametric tests in the next steps. This review provides essential explanations of these concepts without using complex mathematical or statistical equations but with several graphical examples of various statistical terms.

Basic knowledge of statistics is essential for the proper design of clinical studies, data handling methods, appropriate use of analytical tools and the interpretation of the findings. Recognising the importance of the need for competence in biostatistics and research methodology, the Medical Council of India has made it mandatory for the postgraduate medical student to learn research methodology by taking up an online course; and has also made it necessary for faculty in medical colleges to complete the online basic course in biomedical research to get promoted This review focusses on providing an overview regarding various statistical tools commonly used in the design of studies and analysis of data.

Copyright of Econder International Academic Journal is the property of Econder International Academic Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to increasing population growth, inadequate management of surface and subsurface water resources, and the escalation of the water crisis in the coming years, will be inevitable. Therefore, it is necessary to provide scientific and multi-criteria indices capable of more precisely examining the status of surface and subsurface water resources. In this study, the water poverty index (WPI) based on five weighted components of resources, availability, capacity, consumption and environment of the Fasa plain during 2008-2018 were calculated and their trends were evaluated using parametric and nonparametric statistical tests (i.e. Mann-Kendall and Spearman). The results showed that the consumption component (0.47) and the capacity component had highest and lowest role in the WPI index. Trend analysis of the WPI index showed that the WPI based on the linear regression, Mann-Kendall and Spearman tests had a non- significant decreasing trend (with S= -0.01, ZS= -1.03 and ZD= -1.38, respectively). It is natural that the decrease in WPI values ​​reflects an increase in the level of crisis in available water resources. Due to the downward trend of WPI index in the Fasa plain, proper scientific and practical management of water resources is essential to provide the enable long-term sustainable use of resources.

The correct conclusion about the assumptions concerning some phenomena can be obtained only through scientific analysis of statistical data. The scientific procedure of verifying a hypothesis using measurement results is called a statistical test. Depending on whether the hypotheses about the parameters in the feature distribution are tested or the distribution as a whole is tested, a parametric or non-parametric test is selected. The most significant representatives of parametric tests are the Probability Ratio Test, the Neumann - Pearson Lemma and the Bootstrap method, while the Pearson x2 test and the Kolmogorov test are presented as representatives of nonparametric tests. The paper presents the theoretical basis of some methods used in construction of statistical tests with given examples in geodesy.

Short-term hydrothermal scheduling (STHTS) is a highly non-linear, multi-model, non-convex, and multi-dimensional optimization problem that has been worked upon for about 5 decades. Many research articles have been published in solving different test cases of STHTS problem, while establishing the superiority of one type of optimization algorithm over the type, in finding the near global best solution of these complex problems. This paper presents the implementation of an improved version of a variant of the Particle Swarm Optimization algorithm (PSO), known as Accelerated Particle Swarm Optimization (APSO) on three benchmark test cases of STHTS problems. The adaptive and variable nature of the local and global search coefficients for the proposed APSO significantly improve its performance in obtaining the optimal solution for the STHTS test cases. Two of these cases are non-cascaded cases of STHTS problem (NCSTHTS) and one case is cascaded case of STHTS problem (CSTHTS). The results are compared with the results of the previous implementations of the other algorithms as presented in the literature. Due to the stochastic nature of the meta-heuristic algorithms, the parametric and non-parametric statistical tests have been implemented to establish the superiority of results of one type of algorithm over the results of the other type of algorithms.

Basic knowledge of statistics is essential for the proper design of clinical studies, data handling methods, appropriate use of analytical tools and the interpretation of the findings. Recognising the importance of the need for competence in biostatistics and research methodology, the Medical Council of India has made it mandatory for the postgraduate medical student to learn research methodology by taking up an online course; and has also made it necessary for faculty in medical colleges to complete the online basic course in biomedical research to get promoted This review focu sses on providing an overview regarding various statistical tools commonly used in the design of studies and analysis of data. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ciência Agronômica is the property of Revista Ciencia Agronomica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ABSTRACT In experimental statistics, the variability, which allows for conclusions related to experimental accuracy, is usually demonstrated by means of experimental coefficient of variation (CV). The aims of this study were to evaluate the distribution of CV in several experiments of eucalyptus growth, compare the existent classification ranges proposed in the literature (methods proposed by Garcia; Costa, Seraphin and Zimmermann, and Pimentel-Gomes), and define the better one to be used as reference for eucalyptus seedlings cultures in greenhouses or protected cultivation. For that, a blank test with 100 repetitions was conducted and evaluations were performed in 30, 60, 90 and 120 days. Moreover, previous data from literature were collected, for comparison proposes. The obtained results were submitted to Shapiro-Wilk and Kolmogorov-Smirnov tests, and the classification tables were submitted to adherence and heterogeneity tests. As a result, it was possible to conclude that data obtained with the experiments with eucalyptus in the present research did not fit the classification of Pimentel-Gomes and tables obtained by Costa, Seraphin and Zimmermann, while the tables related to the method proposed by Garcia would be preferably used.

This study is based on the statistical analysis of interlaminar fracture toughness of various laminated polymer composites used in aerospace applications through parametric and non-parametric tests. Tukey's, Dunkan's, two-sample t -test, and Kolmogorov–Smirnov tests are used to analyze tensile mode interlaminar fracture toughness of various fiber-reinforced polymer composites obtained by beam theory, modified beam theory, and modified compliance calibration method. Among the studied composite samples, modified compliance calibration method provided the highest average interlaminar fracture toughness, whereas the modified beam theory showed the lowest one. Room temperature cured carbon fiber-reinforced composite samples exhibited higher interlaminar fracture toughness than the autoclave cured samples. Two-sample t -test show that all methods are found coherent with each other in terms of being significantly different. On the contrary, Kolmogorov–Smirnov test revealed no significant difference. Besides, Tukey's and Duncan's tests exhibited almost the same results in regard to significant differences except those obtained by the modified compliance calibration method. Two-sample t -test method should have to be performed in order to observe significant relations rather than Kolmogorov–Smirnov test, since the results of Tukey's and Duncan's tests are only consistent with each other for the beam theory and modified beam theory method. [ABSTRACT FROM AUTHOR]

The primary objective of every investor is to see how his money grows. No matter where one decides to plant or invest his money, there is an inevitable process that follows. The important thing is to closely monitor events and record every detail of information. Unless there is a proper system, issues will build and may become difficult to manage. This is the main reason why there are tools which are necessary to use when planting the seeds for investments. These tools are packed into one parcel and referred to as statistics. The term statistics will refer to descriptive and inferential statistics, probability, parametric and non-parametric tests, time series, and business intelligence. This paper will define statistical gears which are normally used by business managers to gather and analyze data for planning and decision-making. It will further highlight how the elements of statistics can build a strong pillar to run a business and alleviate potential challenges through the use of a tool known as the problem solver.

Abstract Drought indices allow for the characterization of drought events, being the most widely used the Standardized Precipitation Index (SPI), which is recommended by the World Meteorological Organization (WMO) as the standard drought index. The objectives of this study are: i) to characterize the variation of annual SPI index in the state of Rio de Janeiro from 1979 to 2009; ii) to relate ENSO events with the variation of the annual SPI; iii) to identify possible significant trends of rainfall increase and reduction based on the annual SPI; and iv) characterize drought evolution in the SRJ in the 1979–2009 period (Appendix A). For the SPI calculation, a monthly time series with 31 consecutive years was used, based on rainfall data from 99 rainfall stations distributed in eight government regions. Annual SPI data was presented using maps created with ArcGIS software version 10.2.2 and all statistical procedures were calculated in the R environment software. The Shapiro-Wilk (SW) and Bartlett (B) tests characterized rainfall data as being mostly normal and non-homogeneous. The annual SPI showed that the Center South region experienced the highest continuous dry period between 1999 and 2000 and 2002–2003. The annual SPI was assessed with the Mann-Kendall (MK), Curvature Sen (magnitude) and Pettitt (abrupt inflection) tests. The MK test indicated significant trends of annual rainfall increase in the North and Mountainous regions, and significant reduction trends in the Mountainous and Center South regions. Pettitt's test indicated significant abrupt changes in the North, Center South and Mountainous regions. The Curvature Sen test revealed that stations Dois Rios (North) and Cordeiro (Mountainous) registered a greater magnitude in the increase and reduction of the annual SPI, respectively. Graphical abstract Unlabelled Image Highlights • The SPI index is an important tool in the spatiotemporal characterization of drought in the state of Rio de Janeiro. • Events of ENSO were not directly related to the increase or reduction of rainfall in any of the regions of the state. • The influence of ENSO seems to be less representative in terms of rainfall regime alteration in Rio de Janeiro. • Significant trends of annual SPI increase and reduction were identified in regions Center south, Mountainous and North. [ABSTRACT FROM AUTHOR]

Valak is a native vegetable of Iran that includes several species of melanocrommyum subgenus belong to the Allium genus. The study was carried out to evaluate nutrition and antioxidant capacity of different organs of eight wild populations of two Iranian Valaks (Allium akaka S.G. Gmelin and Allium elburzense W.) in different environmental conditions. Results showed that amount of K and Ca in origin habitat was ranged from 182 to 432 and 159 to 252 mg/100g, and in field were 311 to 533 and 193 to 243 mg/100g were obtained, respectively. The total phenol in range of 1.73 to 2.57 g AGE/kgFW, acid ascorbic in range of 6.08 to 8.11 mg/100g FW and antioxidant capacity in range of 2.13 to 3.02 g AAE/kgFW were obtained in the field, while amounts of the compositions in origin habitat were calculated 1.95 to 2.52 g AGE/kgFW, 6.15 to 8.94 mg/100g FW and 1.85 to 3.07 g AAE/kgFW, respectively. The canonical correlation coefficients showed increasing of altitude and soil pH and decreasing of average temperature led to increasing of zinc, iron and potassium values; this has caused increase in plant defense compounds, including phenols. Results showed that calcium, potassium, magnesium, and zinc had the greatest influence on the synthesis of ascorbic acid, phenols and antioxidant capacity than other elements. Based on canonical eigenvalues, all of environmental parameters studied were effective on nutrition value and antioxidant capacity of Valak, which study of each alone, or some of them, cannot be lead to the desired result, and all of these factors together justify the plant growing conditions. [ABSTRACT FROM AUTHOR]

The main objective of this study is to assess the levels of statistical use (basic, intermediate, and advanced) in Applied Linguistics research articles over the past three decades (from 1986 to 2015). The corpus included 4079 quantitative and mixed-methods studies published in ten prominent journals of Applied Linguistics. The articles were analysed and the statistical techniques used were aggregated by two current writers and four PhD students in TEFL. Results showed that descriptive statistics (40.04%) were by far the most commonly used technique followed by one-way ANOVA (14.91%), t-test (10.15%), and Pearson correlation (8.76%). Regarding the sophistication level of statistical use, about 78.77% (n = 4686) of the techniques were classified as basic, 14.49% (n = 862) as intermediate, and 6.74% (n = 401) as advanced. Clearly, most of the techniques were either basic or intermediate, with a significant higher percentage for the former. So, a person with basic knowledge of statistics could understand 69.03% of the papers published during 1986 to 2015. It is discussed that researchers should be updated on recent statistical knowledge if they wish to statistically comprehend research articles published in Applied Linguistics journals. [ABSTRACT FROM AUTHOR]

Despite the wide use of statistics in biomedical research, simple ideas are sometimes misunderstood or misinterpreted by medical research workers, who have only limited knowledge of statistics. This article deals with basic biostatistical concepts and their application to enable postgraduate medical students and researchers to analyze and interpret their study data and to critically interpret published literature. The adequate choice of statistical tests has a strong influence on data interpretation. Understanding this choice is important for critical evaluation of biomedical research. The question often arises on whether to use parametric or nonparametric test. If we are planning a study and trying to determine how many patients/cases to include, a nonparametric test will require a slightly larger sample size to have the same power as the corresponding parametric test. In summary, nonparametric procedures are useful in many cases and necessary in individual, but they are not the perfect solution. Fortunately, the most frequently used parametric analyses have their non-parametric counterparts. This can be useful when the assumptions of a parametric test are violated and we can thus choose a nonparametric alternative instead. [ABSTRACT FROM AUTHOR]

The subject of this paper is to determine the statistical significance of abnormal return that appeared on the New York Stock Exchange after the presidential election in the USA in November 2012. The analysis is focused on securities of the financial institutions listed on the New York Stock Exchange, whereby 85 companies have been included. For the purposes of the analysis a standard methodology of event study has been used. In general, parametric tests show a statistically significant negative impact of the event on stock return, whereby with the nonparametric tests there is no consistent estimation. This paper provides an interpretation of the results. [ABSTRACT FROM PUBLISHER]

Background & Objective: The main purpose of medical researches is to answer a research question or to solve a problem to promote the health of a society The first objective is to answer the research question correctly with minimal errors The second objective is the publication of the results in order to generalize them to a population and use in a wider dimension To achieve these objectives using biostatistics is necessary Despite the importance of biostatistics in medical research researchers have limited understanding of it or due to its complications they refrain from its use Statistics help the researcher in different levels of research including writing a proposal and interpretation of other papers Moreover biostatisticians and epidemiologists also play a very important role in the preparation of manuscripts for publication The present article has eloquently described the most important statistical tests in medical research with applied examples

Two-sample comparisons belonging to basic class of statistical inference are extensively applied in practice. There is a rich statistical literature regarding different parametric methods to address these problems. In this context, most of the powerful techniques are assumed to be based on normally distributed populations. In practice, the alternative distributions of compared samples are commonly unknown. In this case, one can propose a combined test based on the following decision rules: (a) the likelihood-ratio test (LRT) for equality of two normal populations and (b) the Shapiro--Wilk (S-W) test for normality. The rules (a) and (b) can be merged by, e.g., using the Bonferroni correction technique to offer the correct comparison of the samples distribution. Alternatively, we propose the exact density-based empirical likelihood (DBEL) ratio test.We develop the tsc package as the first R package available to perform the two-sample comparisons using the exact test procedures: the LRT; the LRT combined with the S-W test; aswell as the newly developed DBEL ratio test. We demonstrate Monte Carlo (MC) results and a real data example to show an efficiency and excellent applicability of the developed procedure. [ABSTRACT FROM AUTHOR]

The article focuses on data processing and data analysis. It briefly describes basic descriptive statistics - measures of central tendency and variability for continuous data. Particular attention is paid to the correct choice of descriptive statistics of central tendency (arithmetic mean, median, modus) and variability (standard deviation and quantiles) regarding data distribution. It explains basic types of data distribution - symmetric, right skewed and left skewed. Attention is paid to the normal Gaussian distribution and its statistical properties. There are various types of distribution for continuous biomedical variables. It describes the basic steps of hypothesis testing as a method of inductive statistics. It explains the difference between parametric and non-parametric statistical tests. Parametric methods are based on normal distribution and use mean and standard deviation. Non-parametric methods are based on rank. There is an overview of various kinds of parametric and non-parametric tests for hypothesis testing of continuous variables. The importance of choosing the correct parametric or a non-parametric method is emphasized. [ABSTRACT FROM AUTHOR]

Copyright of Research Papers of the Wroclaw University of Economics / Prace Naukowe Uniwersytetu Ekonomicznego we Wroclawiu is the property of Uniwersytet Ekonomiczny we Wroclawiu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Understanding the influences of Atlantic multidecadal oscillation (AMO) and El Niño southern oscillation (ENSO) on regional precipitation extremes and characteristics in the state of Florida is the focus of this study. Exhaustive evaluations of individual and combined influences of these oscillations using, descriptive indices-based assessment of statistically significant changes in rainfall characteristics, identification of spatially varying influences of oscillations on dry and wet spell transition states, antecedent precipitation prior to extreme events, intraevent temporal distribution of precipitation and changes in temporal occurrences of extremes including dry/wet cycles are carried out. Rain gage and gridded precipitation data analysis using parametric hypothesis tests confirm statistically significant changes in the precipitation characteristics from one phase to another of each oscillation and also in coupled phases. Spatially nonuniform and uniform influences of AMO and ENSO, respectively, on precipitation are evident. AMO influences vary in peninsular and continental parts of Florida and the warm (cool) phase of AMO contributes to increased precipitation extremes during wet (dry) season. The influence of ENSO is confined to dry season with El Niño (La Niña) contributing to increase (decrease) in extremes and total precipitation. Wetter antecedent conditions preceding daily extremes are dominant in AMO warm phase compared to the cool and are likely to impact design floods in the region. AMO influence on dry season precipitation extremes is noted for ENSO neutral years. The two oscillations in different phases modulate each other with seasonal and spatially varying impacts and implications on flood control and water supply in the region. [ABSTRACT FROM AUTHOR]

Recent years have witnessed a growing number of published reports that point out the need for reporting various effect size estimates in the context of null hypothesis testing (H0) as a response to a tendency for reporting tests of statistical significance only, with less attention on other important aspects of statistical analysis. In the face of considerable changes over the past several years, neglect to report effect size estimates may be noted in such fields as medical science, psychology, applied linguistics, or pedagogy. Nor have sport sciences managed to totally escape the grips of this suboptimal practice: here statistical analyses in even some of the current research reports do not go much further than computing p-values. The p-value, however, is not meant to provide information on the actual strength of the relationship between variables, and does not allow the researcher to determine the effect of one variable on another. Effect size measures serve this purpose well. While the number of reports containing statistical estimates of effect sizes calculated after applying parametric tests is steadily increasing, reporting effect sizes with non-parametric tests is still very rare. Hence, the main objectives of this contribution are to promote various effect size measures in sport sciences through, once again, bringing to the readers' attention the benefits of reporting them, and to present examples of such estimates with a greater focus on those that can be calculated for non-parametric tests. [ABSTRACT FROM AUTHOR]

Boxplots are a useful and widely used graphical technique to explore data in order to better understand the information we are working with. Boxplots display the first, second and third quartile as well as the interquartile range and outliers of a data set. The information displayed by the boxplot, and most of its variations, is based on the data’s median. However, much of scientific applications analyse and report data using the mean. In this paper, we propose a variation of the classical boxplot that displays information around the mean. Some information about the median is displayed as well.

1. Quadratic entropy (QE) was developed as a fundamental function for measuring the diversity within a collection, such as a community, or population, from indices of abundance and distance among categories, such as species or alleles. Based on a literature review in the fields of genetics, ecology and statistics and new developments, I analyse the potential of this function for biodiversity studies. 2. Quadratic entropy was established as a generalisation of well-known diversity indices, and has been widely used in molecular ecology and genetics research. It is now integrated within more general frameworks for analysing functional and phylogenetic diversity in ecology. 3. Quadratic entropy can be maximised by removing categories, and several collections can share the maximum diversity, even with highly distinct compositions. Clarifying these statements, I identify all potential indices of the abundance of the categories that maximise QE. 4. By quantifying changes in diversity when mixing collections together, QE can measure differences among collections. Here, I provide a geometric interpretation of these differences that demonstrates their relevance as classical geometric distances. 5. A critical aspect of these distances is obtained if QE is strictly concave; that is, diversity always strictly increases by mixing distinct collections together. More generally, QE can be used to evaluate the effects of various factors on diversity in a framework designated ANOQE (analysis of QE). Generalising ANOVA (analysis of variance), ANOQE uses QE to measure distances between centroids. 6. Importantly, QE is estimated from sampled data and thus requires estimators. Based on these estimators, tests have been developed to compare levels of diversity. Tests of factor effects are evaluated by parametric, jackknife, bootstrap and permutational approaches. However, the procedures associated with these tests that have been suggested thus far only treat a few factors. 7. There is an urgent need for the development of such approaches in biology to deal with experimental factors, observed population and community structure, and different spatial and temporal scales. Together, QE and the ANOQE procedure are likely to have a critical impact on all scientific disciplines interested in any form of diversity. [ABSTRACT FROM AUTHOR]

Dialysis is a life saving procedure and recommended way of treatment for end stage kidney diseases. Transplantation can also be useful source but it is restricted by financial limitations especially in developing countries like Pakistan. A little work has been done in literature regarding the estimated survival time of dialysis patients in Pakistan. So, this study has estimated the median survival time of male/females patients separately by parametric and nonparametric approaches. Moreover, comparison of survival time to patients (⩽ 50 years and >50 years) was also compared. As censoring is an important part of the survival data which causes insensitivity to the usual procedures of analysis. Frequently, in modeling the survival data, most of the time we have no prior information about the theoretical distribution of survival time, that's why, nonparametric methods are commonly used. The significance of this study is the fitting of probability distribution of real life time data of dialysis patients which is not done before. It is very laborious job to fit an appropriate distribution of the data and estimation of parameters. We find that the probability distribution of our real life time data is weibull distribution. Finding suggested that the Kaplan-Meier method and weibull model based on Anderson-Darling test provided a very close estimate of the survival function in both genders and age groups. On the average survival time in males is relatively high but not statistically different from females. [ABSTRACT FROM AUTHOR]

In randomized clinical trials of antiepileptic drags (AEDs), the seizure frequency per x days during baseline and treatment phase periods are recorded to evaluate efficacy of a drag. The seizure frequency data are often nonnormal, and hence an appropriate mathematical transformation is necessary for a statistical analysis. The most commonly used transformations in AED development research are (a) log-transformation of the seizure frequency data, and (b) calculation of percentage change (PC) from baseline in seizure frequency. The log-transformed postbaseline seizure frequency data are analyzed using a parametric ANCOVA model induding the log-transformed baseline data as a covariate and treatment group as a factor in the modal. The PC data are analyzed using either a Wilcoxon rank-sum test on the PCs, or ANOVA/ANCOVA analysis on the ranks of PCs including treatment groups us a factor and any covariate of interest in the modal. A limited number of research works is available in the literature regarding a choice of log-transformed or PC of seizure frequency data in the statistical analyses. In this research, an attempt is made to evaluate the impacts of choosing either of the two transformations on seizure frequency data of AED trials. [ABSTRACT FROM AUTHOR]

Boxplots are a useful and widely used graphical technique to explore data in order to better understand the information we are working with. Boxplots display the first, second and third quartile as well as the interquartile range and outliers of a data set. The information displayed by the boxplot, and most of its variations, is based on the data's median. However, much of scientific applications analyse and report data using the mean. In this paper, we propose a variation of the classical boxplot that displays information around the mean. Some information about the median is displayed as well. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest for the experimental analysis in the field of evolutionary algorithms. It is noticeable due to the existence of numerous papers which analyze and propose different types of problems, such as the basis for experimental comparisons of algorithms, proposals of different methodologies in comparison or proposals of use of different statistical techniques in algorithms’ comparison. In this paper, we focus our study on the use of statistical techniques in the analysis of evolutionary algorithms’ behaviour over optimization problems. A study about the required conditions for statistical analysis of the results is presented by using some models of evolutionary algorithms for real-coding optimization. This study is conducted in two ways: single-problem analysis and multiple-problem analysis. The results obtained state that a parametric statistical analysis could not be appropriate specially when we deal with multiple-problem results. In multiple-problem analysis, we propose the use of non-parametric statistical tests given that they are less restrictive than parametric ones and they can be used over small size samples of results. As a case study, we analyze the published results for the algorithms presented in the CEC’2005 Special Session on Real Parameter Optimization by using non-parametric test procedures. [ABSTRACT FROM AUTHOR]