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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
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Index of orthodontic treatment need in children from the Niš region [PDF]
Vojnosanitetski Pregled, 2015 Background/Aim. The Index of Orthodontic Treatment Need (IOTN) is a scoring system for malocclusion that consists of the two independent components: Denal Health Component (DHC) and Aesthetic Component (AC).
Janošević Predrag +5 more
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Comprehensive guidelines for appropriate statistical analysis methods in research [PDF]
Korean Journal of AnesthesiologyBackground 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 +2 more
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Equivalent statistics and data interpretation [PDF]
Behavior Research Methods, 2016 Recent reform efforts in psychological science have led to a plethora of choices for scientists to analyze their data. A scientist making an inference about their data must now decide whether to report a p value, summarize the data with a standardized effect size and its confidence interval, report a Bayes Factor, or use other model comparison methods.
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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 +2 more
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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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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 +2 more
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Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation
AQUATIC SCIENCES AND ENGINEERING, 2019 The study area is focused on the Mariana Trench, west Pacific Ocean. The research aim is to investigate correlation between various factors, such as bathymetric depths, geomorphic shape, geographic location on four tectonic plates of the sampling points ...
Polina Lemenkova
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Interpretation and identification of within-unit and cross-sectional variation in panel data models
PLoS ONE, 2020 While fixed effects (FE) models are often employed to address potential omitted variables, we argue that these models’ real utility is in isolating a particular dimension of variance from panel data for analysis.
J. Kropko, R. Kubinec
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Statistical Inference: The Big Picture [PDF]
Statistical Science 2011, Vol. 26, No. 1, 1-9, 2011 Statistics has moved beyond the frequentist-Bayesian controversies of the past. Where does this leave our ability to interpret results? I suggest that a philosophy compatible with statistical practice, labeled here statistical pragmatism, serves as a foundation for inference.
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