Topic
Sample (statistics)
About: Sample (statistics) is a research topic. Over the lifetime, 15874 publications have been published within this topic receiving 408939 citations. The topic is also known as: statistical sample & data sample.
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TL;DR: Which elements of this often-quoted strategy for graphical representation of multivariate (multi-species) abundance data have proved most useful in practical assessment of community change resulting from pollution impact are identified.
Abstract: In the early 1980s, a strategy for graphical representation of multivariate (multi-species) abundance data was introduced into marine ecology by, among others, Field, et al. (1982). A decade on, it is instructive to: (i) identify which elements of this often-quoted strategy have proved most useful in practical assessment of community change resulting from pollution impact; and (ii) ask to what extent evolution of techniques in the intervening years has added self-consistency and comprehensiveness to the approach. The pivotal concept has proved to be that of a biologically-relevant definition of similarity of two samples, and its utilization mainly in simple rank form, for example ‘sample A is more similar to sample B than it is to sample C’. Statistical assumptions about the data are thus minimized and the resulting non-parametric techniques will be of very general applicability. From such a starting point, a unified framework needs to encompass: (i) the display of community patterns through clustering and ordination of samples; (ii) identification of species principally responsible for determining sample groupings; (iii) statistical tests for differences in space and time (multivariate analogues of analysis of variance, based on rank similarities); and (iv) the linking of community differences to patterns in the physical and chemical environment (the latter also dictated by rank similarities between samples). Techniques are described that bring such a framework into place, and areas in which problems remain are identified. Accumulated practical experience with these methods is discussed, in particular applications to marine benthos, and it is concluded that they have much to offer practitioners of environmental impact studies on communities.
12,446 citations
10,457 citations
TL;DR: In this article, a test of the hypothesis that the samples are from the same population may be made by ranking the observations from from 1 to Σn i (giving each observation in a group of ties the mean of the ranks tied for), finding the C sums of ranks, and computing a statistic H. Under the stated hypothesis, H is distributed approximately as χ2(C − 1), unless the samples were too small, in which case special approximations or exact tables are provided.
Abstract: Given C samples, with n i observations in the ith sample, a test of the hypothesis that the samples are from the same population may be made by ranking the observations from from 1 to Σn i (giving each observation in a group of ties the mean of the ranks tied for), finding the C sums of ranks, and computing a statistic H. Under the stated hypothesis, H is distributed approximately as χ2(C – 1), unless the samples are too small, in which case special approximations or exact tables are provided. One of the most important applications of the test is in detecting differences among the population means.* * Based in part on research supported by the Office of Naval Research at the Statistical Research Center, University of Chicago.
9,365 citations
TL;DR: A more progressive resource for sample-based studies, meta-analyses, and case studies in sports medicine and exercise science is presented, and forthright advice on controversial or novel issues is offered.
Abstract: Statistical guidelines and expert statements are now available to assist in the analysis and reporting of studies in some biomedical disciplines. We present here a more progressive resource for sample-based studies, meta-analyses, and case studies in sports medicine and exercise science. We offer forthright advice on the following controversial or novel issues: using precision of estimation for inferences about population effects in preference to null-hypothesis testing, which is inadequate for assessing clinical or practical importance; justifying sample size via acceptable precision or confidence for clinical decisions rather than via adequate power for statistical significance; showing SD rather than SEM, to better communicate the magnitude of differences in means and nonuniformity of error; avoiding purely nonparametric analyses, which cannot provide inferences about magnitude and are unnecessary; using regression statistics in validity studies, in preference to the impractical and biased limits of agreement; making greater use of qualitative methods to enrich sample-based quantitative projects; and seeking ethics approval for public access to the depersonalized raw data of a study, to address the need for more scrutiny of research and better meta-analyses. Advice on less contentious issues includes the following: using covariates in linear models to adjust for confounders, to account for individual differences, and to identify potential mechanisms of an effect; using log transformation to deal with nonuniformity of effects and error; identifying and deleting outliers; presenting descriptive, effect, and inferential statistics in appropriate formats; and contending with bias arising from problems with sampling, assignment, blinding, measurement error, and researchers' prejudices. This article should advance the field by stimulating debate, promoting innovative approaches, and serving as a useful checklist for authors, reviewers, and editors.
6,467 citations
Posted Content•
TL;DR: In this paper, the authors evaluate alternative models for detecting earnings management by comparing the specification and power of commonly used test statistics across the measures of discretionary accruals generated by each model.
Abstract: This paper evaluates alternative models for detecting earnings management. The paper restricts itself to models that assume the construct being managed is discretionary accruals, since such models are commonly used in the extant accounting literature. Existing models range from simple models in which discretionary accruals are measured as total accruals, to more sophisticated models that separate total accruals into a discretionary and a non-discretionary component. Prior to this paper, there had been no systematic evidence bearing on the relative performance of these alternative models at detecting earnings management. This paper evaluates the relative performance of the competing models by comparing the specification and power of commonly used test statistics across the measures of discretionary accruals generated by each model. The specification of the test statistics is evaluated by examining the frequency with which they generate type I errors for a random sample of firm-years and for samples of firm-years with extreme financial performance. We focus on samples with extreme financial performance because the stimuli investigated in previous research are frequently correlated with financial performance. The first sample of firms are targeted by the Securities and Exchange Commission for allegedly overstating annual earnings and the second sample is created by artificially introducing earnings management into a random sample of firms.
6,217 citations