Results 11 to 20 of about 1,676,124 (274)

Common pitfalls in statistical analysis: The use of correlation techniques

Perspectives in Clinical Research, 2016
Correlation is a statistical technique which shows whether and how strongly two continuous variables are related. In this article, which is the eighth part in a series on ′Common pitfalls in Statistical Analysis′, we look at the interpretation of the ...
Rakesh Aggarwal, Priya Ranganathan
doaj   +1 more source

Comprehensive guidelines for appropriate statistical analysis methods in research [PDF]

Korean Journal of Anesthesiology
Background The selection of statistical analysis methods in research is a critical and nuanced task that requires a scientific and rational approach. Aligning the chosen method with the specifics of the research design and hypothesis is paramount, as it ...
Jonghae Kim, Dong Hyuck Kim, Sang Gyu Kwak   +2 more
doaj   +1 more source

A guide to modern statistical analysis of immunological data. [PDF]

, 2007
BACKGROUND: The number of subjects that can be recruited in immunological studies and the number of immunological parameters that can be measured has increased rapidly over the past decade and is likely to continue to expand.
Barreto, Mauricio L, Cooper Philip J, Genser Bernd, Cooper, Philip J, Yazdanbakhsh, Maria, Yazdanbakhsh Maria, Rodrigues, LC, Rodrigues Laura C, Yazdanbakhsh, M, Genser, Bernd, Barreto, ML, Barreto Mauricio L, Rodrigues, Laura C, Cooper, PJ, Genser, B   +14 more
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Common pitfalls in statistical analysis: Clinical versus statistical significance

Perspectives in Clinical Research, 2015
In clinical research, study results, which are statistically significant are often interpreted as being clinically important. While statistical significance indicates the reliability of the study results, clinical significance reflects its impact on ...
Priya Ranganathan, C S Pramesh, Marc Buyse   +2 more
doaj   +1 more source

Common pitfalls in statistical analysis: The perils of multiple testing

Perspectives in Clinical Research, 2016
Multiple testing refers to situations where a dataset is subjected to statistical testing multiple times - either at multiple time-points or through multiple subgroups or for multiple end-points. This amplifies the probability of a false-positive finding.
Priya Ranganathan, C S Pramesh, Marc Buyse   +2 more
doaj   +1 more source

Data of the article: The Goodness-of-Fit of Statistical Models for Trace Length Distributions and Their Interpretation

, 2023
These are MATLAB data files of the article: The Goodness-of-Fit of Statistical Models for Trace Length Distributions and Their Interpretation (Kim et al., 2024)Detailed information about the dataset is presented in the original article.
Kim, J (via Mendeley Data)
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Nonparametric statistical tests for the continuous data: the basic concept and the practical use [PDF]

Korean Journal of Anesthesiology, 2016
Conventional statistical tests are usually called parametric tests. Parametric tests are used more frequently than nonparametric tests in many medical articles, because most of the medical researchers are familiar with and the statistical software ...
Francis Sahngun Nahm
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Iterative Group Analysis (iGA): A simple tool to enhance sensitivity and facilitate interpretation of microarray experiments [PDF]

, 2004
BACKGROUND: The biological interpretation of even a simple microarray experiment can be a challenging and highly complex task. Here we present a new method (Iterative Group Analysis) to facilitate, improve, and accelerate this process.
Amtmann, A., Herzyk, P., Anna Amtmann, Amtmann, Anna, Herzyk, Pawel, Breitling, Rainer, Breitling, R., Rainer Breitling, Pawel Herzyk   +8 more
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Atypical Teratoid Rhabdoid Tumor: Two Case Reports and an Analysis of Adult Cases with Implications for Pathophysiology and Treatment

Frontiers in Neurology, 2017
We present the first quantitative analysis of atypical teratoid rhabdoid tumors (ATRT) in adults, including two patients from our own institutions. These are of interest as one occurred during pregnancy and one is a long-term survivor.
Christopher Dardis, Jared Yeo, Kelly Milton, Lynn S. Ashby, Kris A. Smith, Shwetal Mehta, Emad Youssef, Jenny Eschbacher, Kathy Tucker, Laughlin Dawes, Neil Lambie, Elizabeth Algar, Elizabeth Hovey, Elizabeth Hovey   +13 more
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Is the Bland-Altman plot method useful without inferences for accuracy, precision, and agreement? [PDF]

Revista de Saúde Pública
OBJECTIVE This study aims to propose a comprehensive alternative to the Bland-Altman plot method, addressing its limitations and providing a statistical framework for evaluating the equivalences of measurement techniques.
Paulo Sergio Panse Silveira, Joaquim Edson Vieira, José de Oliveira Siqueira   +2 more
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