Type I and type II errors ≈ Type I and type II errors
View article: <b>lmerTest</b> Package: Tests in Linear Mixed Effects Models
<b>lmerTest</b> Package: Tests in Linear Mixed Effects Models Open
One of the frequent questions by users of the mixed model function lmer of the lme4 package has been: How can I get p values for the F and t tests for objects returned by lmer? The lmerTest package extends the 'lmerMod' class of the lme4 p…
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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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Balancing Type I error and power in linear mixed models Open
Linear mixed-effects models have increasingly replaced mixed-model analyses of variance for statistical inference in factorial psycholinguistic experiments. Although LMMs have many advantages over ANOVA, like ANOVAs, setting them up for da…
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Bias due to participant overlap in two‐sample Mendelian randomization Open
Mendelian randomization analyses are often performed using summarized data. The causal estimate from a one‐sample analysis (in which data are taken from a single data source) with weak instrumental variables is biased in the direction of t…
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Non-normal data: Is ANOVA still a valid option? Open
The results showed that in terms of Type I error the F-test was robust in 100% of the cases studied, independently of the manipulated conditions.
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SISVAR: A COMPUTER ANALYSIS SYSTEM TO FIXED EFFECTS SPLIT PLOT TYPE DESIGNS Open
This paper presents a special capability of Sisvar to deal with fixed effect models with several restriction in the randomization procedure. These restrictions lead to models with fixed treatment effects, but with several random errors. On…
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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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Four assumptions of multiple regression that researchers should always test Open
Most statistical tests rely upon certain assumptions about the variables used in the analysis. When these assumptions are not met the results may not be trustworthy, resulting in a Type I or Type II error, or over- or under-estimation of s…
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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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A comparison of robust Mendelian randomization methods using summary data Open
The number of Mendelian randomization (MR) analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome‐wide association studies, and the desire to obtain more precise estimates of …
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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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Statistically Controlling for Confounding Constructs Is Harder than You Think Open
Social scientists often seek to demonstrate that a construct has incremental validity over and above other related constructs. However, these claims are typically supported by measurement-level models that fail to consider the effects of m…
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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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Understanding one-way ANOVA using conceptual figures Open
Analysis of variance (ANOVA) is one of the most frequently used statistical methods in medical research. The need for ANOVA arises from the error of alpha level inflation, which increases Type 1 error probability (false positive) and is ca…
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A Statistical Significance Test for Necessary Condition Analysis Open
In this article, we present a statistical significance test for necessary conditions. This is an elaboration of necessary condition analysis (NCA), which is a data analysis approach that estimates the necessity effect size of a condition X…
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Methods for testing publication bias in ecological and evolutionary meta‐analyses Open
Publication bias threatens the validity of quantitative evidence from meta‐analyses as it results in some findings being overrepresented in meta‐analytic datasets because they are published more frequently or sooner (e.g. ‘positive’ result…
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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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Modeling Clustered Data with Very Few Clusters Open
Small-sample inference with clustered data has received increased attention recently in the methodological literature, with several simulation studies being presented on the small-sample behavior of many methods. However, nearly all previo…
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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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Bias caused by sampling error in meta-analysis with small sample sizes Open
Cautions are needed to perform meta-analyses with small sample sizes. The reported within-study variances may not be simply treated as the true variances, and their sampling error should be fully considered in such meta-analyses.
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Small-Sample Methods for Cluster-Robust Variance Estimation and Hypothesis Testing in Fixed Effects Models Open
In longitudinal panels and other regression models with unobserved effects, fixed effects estimation is often paired with cluster-robust variance estimation (CRVE) in order to account for heteroskedasticity and un-modeled dependence among …
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Sample Size Guideline for Correlation Analysis Open
Correlation analysis is a common statistical analysis in various fields. The aim is usually to determine to what extent two numerical variables are correlated with each other. One of the issues that are important to be considered before co…
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Why Welch's test is Type I error robust Open
The comparison of two means is one of the most commonly applied statistical procedures in psychology. The independent samples t-test corrected for unequal variances is commonly known as Welchs test, and is widely considered to be a robust …
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Taking Parametric Assumptions Seriously: Arguments for the Use of Welch’s <i>F</i>-test instead of the Classical <i>F</i>-test in One-Way ANOVA Open
Student's t-test and classical F-test ANOVA rely on the assumptions that two or more samples are independent, and that independent and identically distributed residuals are normal and have equal variances between groups. We focus on the as…
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The Romano–Wolf multiple-hypothesis correction in Stata Open
When considering multiple-hypothesis tests simultaneously, standard statistical techniques will lead to overrejection of null hypotheses unless the multiplicity of the testing framework is explicitly considered. In this article, we discuss…
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Impact of Sample Size and Variability on the Power and Type I Error Rates of Equivalence Tests: A Simulation Study Open
The question of equivalence between two or more groups is frequently of interest to many applied researchers. Equivalence testing is a statistical method designed to provide evidence that groups are comparable by demonstrating that the mea…
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Multiple comparison test by Tukey’s honestly significant difference (HSD): Do the confident level control type I error Open
Examining a huge amount of data is a typical issue in any research process. However, different statistical processes and techniques play essential role to derive a meaningful conclusion from the presented enormous data. Control of type I e…
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Fisher’s exact approach for post hoc analysis of a chi-squared test Open
This research is motivated by one of our survey studies to assess the potential influence of introducing zebra mussels to the Lake Mead National Recreation Area, Nevada. One research question in this study is to investigate the association…
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Cluster randomized trials with a small number of clusters: which analyses should be used? Open
Small-sample corrections or variance-weighted cluster-level analyses are recommended for the analysis of continuous outcomes in CRTs with a small number of clusters. The use of these corrections should be incorporated into the sample size …
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Power Analysis, Sample Size, and Assessment of Statistical Assumptions—Improving the Evidential Value of Lighting Research Open
The reporting of accurate and appropriate conclusions is an essential aspect of scientific research, and failure in this endeavor can threaten the progress of cumulative knowledge. This is highlighted by the current reproducibility crisis,…