Statistical power
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Sample size determination and power analysis using the G*Power software Open
Appropriate sample size calculation and power analysis have become major issues in research and publication processes. However, the complexity and difficulty of calculating sample size and power require broad statistical knowledge, there i…
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Repeated Measures Correlation Open
Repeated measures correlation (rmcorr) is a statistical technique for determining the common within-individual association for paired measures assessed on two or more occasions for multiple individuals. Simple regression/correlation is oft…
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Equivalence Tests Open
Scientists should be able to provide support for the absence of a meaningful effect. Currently, researchers often incorrectly conclude an effect is absent based a nonsignificant result. A widely recommended approach within a frequentist fr…
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Applied Psychometrics: Sample Size and Sample Power Considerations in Factor Analysis (EFA, CFA) and SEM in General Open
Adequate statistical power contributes to observing true relationships in a dataset. With a thoughtful power analysis, the adequate but not excessive sample could be detected. Therefore, this paper reviews the issue of what sample size and…
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Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies Open
Calculating the sample size in scientific studies is one of the critical issues as regards the scientific contribution of the study. The sample size critically affects the hypothesis and the study design, and there is no straightforward wa…
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Sample Size Justification Open
An important step when designing an empirical study is to justify the sample size that will be collected. The key aim of a sample size justification for such studies is to explain how the collected data is expected to provide valuable info…
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Trial Sequential Analysis in systematic reviews with meta-analysis Open
Trial Sequential Analysis represents analysis of meta-analytic data, with transparent assumptions, and better control of type I and type II errors than the traditional meta-analysis using naïve unadjusted confidence intervals.
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Using the Student's t-test with extremely small sample sizes Open
Researchers occasionally have to work with an extremely small sample size, defined herein as N ≤ 5. Some methodologists have cautioned against using the t-test when the sample size is extremely small, whereas others have suggested that u…
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How Many Participants Do We Have to Include in Properly Powered Experiments? A Tutorial of Power Analysis with Reference Tables Open
Given that an effect size of d = .4 is a good first estimate of the smallest effect size of interest in psychological research, we already need over 50 participants for a simple comparison of two within-participants conditions if we want t…
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Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature Open
[This corrects the article DOI: 10.1371/journal.pbio.2000797.].
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Statistical data preparation: management of missing values and outliers Open
Missing values and outliers are frequently encountered while collecting data. The presence of missing values reduces the data available to be analyzed, compromising the statistical power of the study, and eventually the reliability of its …
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Effect Size Guidelines, Sample Size Calculations, and Statistical Power in Gerontology Open
Background and Objectives Researchers typically use Cohen’s guidelines of Pearson’s r = .10, .30, and .50, and Cohen’s d = 0.20, 0.50, and 0.80 to interpret observed effect sizes as small, medium, or large, respectively. However, these gui…
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Requirements for Minimum Sample Size for Sensitivity and Specificity Analysis Open
Sensitivity and specificity analysis is commonly used for screening and diagnostic tests. The main issue researchers face is to determine the sufficient sample sizes that are related with screening and diagnostic studies. Although the form…
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A solution to minimum sample size for regressions Open
Regressions and meta-regressions are widely used to estimate patterns and effect sizes in various disciplines. However, many biological and medical analyses use relatively low sample size (N), contributing to concerns on reproducibility. W…
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Power analysis for random‐effects meta‐analysis Open
One of the reasons for the popularity of meta‐analysis is the notion that these analyses will possess more power to detect effects than individual studies. This is inevitably the case under a fixed‐effect model. However, the inclusion of t…
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Violating the normality assumption may be the lesser of two evils Open
When data are not normally distributed, researchers are often uncertain whether it is legitimate to use tests that assume Gaussian errors, or whether one has to either model a more specific error structure or use randomization techniques. …
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Simulation-Based Power Analysis for Factorial Analysis of Variance Designs Open
Researchers often rely on analysis of variance (ANOVA) when they report results of experiments. To ensure that a study is adequately powered to yield informative results with an ANOVA, researchers can perform an a priori power analysis. Ho…
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Why, When and How to Adjust Your P Values? Open
Currently, numerous papers are published reporting analysis of biological data at different omics levels by making statistical inferences. Of note, many studies, as those published in this Journal, report association of gene(s) at the geno…
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Experiments with More Than One Random Factor: Designs, Analytic Models, and Statistical Power Open
Traditional methods of analyzing data from psychological experiments are based on the assumption that there is a single random factor (normally participants) to which generalization is sought. However, many studies involve at least two ran…
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Statistical harmonization corrects site effects in functional connectivity measurements from multi‐site fMRI data Open
Acquiring resting‐state functional magnetic resonance imaging (fMRI) datasets at multiple MRI scanners and clinical sites can improve statistical power and generalizability of results. However, multi‐site neuroimaging studies have reported…
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Linear models enable powerful differential activity analysis in massively parallel reporter assays Open
Together, these results inform recommendations for differential analysis, general group comparisons, and power analysis and will help improve design and analysis of MPRA experiments.
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Contextual sensitivity in scientific reproducibility Open
Significance Scientific progress requires that findings can be reproduced by other scientists. However, there is widespread debate in psychology (and other fields) about how to interpret failed replications. Many have argued that contextua…
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Basic statistical tools in research and data analysis Open
Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningle…
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Re-evaluation of the Power of the Mann-Kendall Test for Detecting Monotonic Trends in Hydrometeorological Time Series Open
The Mann-Kendall (MK) statistical test has been widely applied in the trend detection of the hydrometeorological time series. Previous studies have mainly focused on the null hypothesis of “no trend” or the “Type I Error.” However, few stu…
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The relation between statistical power and inference in fMRI Open
Statistically underpowered studies can result in experimental failure even when all other experimental considerations have been addressed impeccably. In fMRI the combination of a large number of dependent variables, a relatively small numb…
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Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics Open
Integrating single nucleotide polymorphism (SNP) p-values from genome-wide association studies (GWAS) across genes and pathways is a strategy to improve statistical power and gain biological insight. Here, we present Pascal (Pathway scorin…
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Sample Size and its Importance in Research Open
The sample size for a study needs to be estimated at the time the study is proposed; too large a sample is unnecessary and unethical, and too small a sample is unscientific and also unethical. The necessary sample size can be calculated, u…
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The reign of the <i>p</i> -value is over: what alternative analyses could we employ to fill the power vacuum? Open
The p -value has long been the figurehead of statistical analysis in biology, but its position is under threat. p is now widely recognized as providing quite limited information about our data, and as being easily misinterpreted. Many biol…
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Reproducibility of R‐fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes Open
Concerns regarding reproducibility of resting‐state functional magnetic resonance imaging (R‐fMRI) findings have been raised. Little is known about how to operationally define R‐fMRI reproducibility and to what extent it is affected by mul…
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Dream: powerful differential expression analysis for repeated measures designs Open
Summary Large-scale transcriptome studies with multiple samples per individual are widely used to study disease biology. Yet, current methods for differential expression are inadequate for cross-individual testing for these repeated measur…