scholarly journals Genotypic diversity: estimation and prediction in samples.

Genetics ◽  
1988 ◽  
Vol 118 (4) ◽  
pp. 705-711 ◽  
Author(s):  
J A Stoddart ◽  
J F Taylor
Keyword(s):  
Goodness Of Fit ◽  
Type I Error ◽  
Genotypic Diversity ◽  
Error Rates ◽  
Type I ◽  
Type Ii ◽  
Type I Error Rates ◽  
Goodness Of Fit Tests ◽  
High Level ◽  
Better Than

Abstract We show that a commonly used statistic of genotypic diversity can be used to reflect one form of deviation from panmixia, viz. clonal reproduction, by comparing observed and predicted sample statistics. The characteristics of the statistic, in particular its relationship with population genotypic diversity, are formalised and a method of predicting the genotypic diversity of a sample drawn from a panmictic population using allelic frequencies and sample size is developed. The sensitivity of some possible tests of significance of the deviation from panmictic expectations is examined using computer simulations. Goodness-of-fit tests are robust but produce an unacceptably high level of type II error. With means and variances calculated either from Monte Carlo simulations or from distributional and series approximations, t-tests perform better than goodness-of-fit tests. Under simulation, both forms of t-test exhibit acceptable rates of type I error. Rates of type II are usually large when allele frequencies are severely skewed although the latter test performs the better in those conditions.

A New and Simpler Approximation for ANOVA Under Variance Heterogeneity

1994 ◽  
Vol 19 (2) ◽  
pp. 91-101 ◽  
Author(s):  
Ralph A. Alexander ◽  
Diane M. Govern
Keyword(s):  
Type I Error ◽  
Order Approximation ◽  
Error Rates ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Tail Probabilities ◽  
Type I Error Rates ◽  
Heterogeneity Of Variance ◽  
The Face

A new approximation is proposed for testing the equality of k independent means in the face of heterogeneity of variance. Monte Carlo simulations show that the new procedure has Type I error rates that are very nearly nominal and Type II error rates that are quite close to those produced by James’s (1951) second-order approximation. In addition, it is computationally the simplest approximation yet to appear, and it is easily applied to Scheffé (1959) -type multiple contrasts and to the calculation of approximate tail probabilities.

scholarly journals Analysis of type I and II error rates of Bayesian and frequentist parametric and nonparametric two-sample hypothesis tests under preliminary assessment of normality

2020 ◽  
Author(s):  
Riko Kelter
Keyword(s):  
Null Hypothesis ◽  
Error Control ◽  
Type I Error ◽  
Error Rates ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Preliminary Assessment ◽  
Type I Error Rates ◽  
Practical Research

Abstract Testing for differences between two groups is among the most frequently carried out statistical methods in empirical research. The traditional frequentist approach is to make use of null hypothesis significance tests which use p values to reject a null hypothesis. Recently, a lot of research has emerged which proposes Bayesian versions of the most common parametric and nonparametric frequentist two-sample tests. These proposals include Student’s two-sample t-test and its nonparametric counterpart, the Mann–Whitney U test. In this paper, the underlying assumptions, models and their implications for practical research of recently proposed Bayesian two-sample tests are explored and contrasted with the frequentist solutions. An extensive simulation study is provided, the results of which demonstrate that the proposed Bayesian tests achieve better type I error control at slightly increased type II error rates. These results are important, because balancing the type I and II errors is a crucial goal in a variety of research, and shifting towards the Bayesian two-sample tests while simultaneously increasing the sample size yields smaller type I error rates. What is more, the results highlight that the differences in type II error rates between frequentist and Bayesian two-sample tests depend on the magnitude of the underlying effect.

scholarly journals Investigating the Behaviors of M2 and RMSEA2 in Fitting a Unidimensional Model to Multidimensional Data

2017 ◽  
Vol 41 (8) ◽  
pp. 632-644
Author(s):  
Jie Xu ◽  
Insu Paek ◽  
Yan Xia
Keyword(s):  
Goodness Of Fit ◽  
Type I Error ◽  
Error Rates ◽  
Multidimensional Data ◽  
Ratio Test ◽  
Type I ◽  
Limited Information ◽  
Test Statistic ◽  
Goodness Of Fit Test ◽  
Goodness Of Fit Tests

It has been widely known that the Type I error rates of goodness-of-fit tests using full information test statistics, such as Pearson’s test statistic χ2 and the likelihood ratio test statistic G2, are problematic when data are sparse. Under such conditions, the limited information goodness-of-fit test statistic M2 is recommended in model fit assessment for models with binary response data. A simulation study was conducted to investigate the power and Type I error rate of M2 in fitting unidimensional models to many different types of multidimensional data. As an additional interest, the behavior of RMSEA2 was also examined, which is the root mean square error approximation (RMSEA) based on M2. Findings from the current study showed that M2 and RMSEA2 are sensitive in detecting the misfits due to varying slope parameters, the bifactor structure, and the partially (or completely) simple structure for multidimensional data, but not the misfits due to the within-item multidimensional structures.

scholarly journals Permutation tests for hypothesis testing with animal social data: problems and potential solutions

2020 ◽  
Author(s):  
Damien R. Farine ◽  
Gerald G. Carter
Keyword(s):  
Type I Error ◽  
Permutation Test ◽  
Permutation Tests ◽  
Error Rates ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Type I Error Rates ◽  
Social Network Data ◽  
Elevated Type

ABSTRACTGenerating insights about a null hypothesis requires not only a good dataset, but also statistical tests that are reliable and actually address the null hypothesis of interest. Recent studies have found that permutation tests, which are widely used to test hypotheses when working with animal social network data, can suffer from high rates of type I error (false positives) and type II error (false negatives).Here, we first outline why pre-network and node permutation tests have elevated type I and II error rates. We then propose a new procedure, the double permutation test, that addresses some of the limitations of existing approaches by combining pre-network and node permutations.We conduct a range of simulations, allowing us to estimate error rates under different scenarios, including errors caused by confounding effects of social or non-social structure in the raw data.We show that double permutation tests avoid elevated type I errors, while remaining sufficiently sensitive to avoid elevated type II errors. By contrast, the existing solutions we tested, including node permutations, pre-network permutations, and regression models with control variables, all exhibit elevated errors under at least one set of simulated conditions. Type I error rates from double permutation remain close to 5% in the same scenarios where type I error rates from pre-network permutation tests exceed 30%.The double permutation test provides a potential solution to issues arising from elevated type I and type II error rates when testing hypotheses with social network data. We also discuss other approaches, including restricted node permutations, testing multiple null hypotheses, and splitting large datasets to generate replicated networks, that can strengthen our ability to make robust inferences. Finally, we highlight ways that uncertainty can be explicitly considered during the analysis using permutation-based or Bayesian methods.

scholarly journals Control of Type I Error Rates in Bayesian Sequential Designs

2019 ◽  
Vol 14 (2) ◽  
pp. 399-425 ◽  
Author(s):  
Haolun Shi ◽  
Guosheng Yin
Keyword(s):  
Type I Error ◽  
Error Rates ◽  
Type I ◽  
Sequential Designs ◽  
Type I Error Rates

scholarly journals Sisvar: a Guide for its Bootstrap procedures in multiple comparisons

2014 ◽  
Vol 38 (2) ◽  
pp. 109-112 ◽  
Author(s):  
Daniel Furtado Ferreira
Keyword(s):  
Scientific Community ◽  
Type I Error ◽  
Multiple Comparisons ◽  
Error Rates ◽  
Type I ◽  
Type I Error Rates ◽  
Statistical Analysis System ◽  
Scientific Results ◽  
Analysis System ◽  
Multiple Comparison Procedures

Sisvar is a statistical analysis system with a large usage by the scientific community to produce statistical analyses and to produce scientific results and conclusions. The large use of the statistical procedures of Sisvar by the scientific community is due to it being accurate, precise, simple and robust. With many options of analysis, Sisvar has a not so largely used analysis that is the multiple comparison procedures using bootstrap approaches. This paper aims to review this subject and to show some advantages of using Sisvar to perform such analysis to compare treatments means. Tests like Dunnett, Tukey, Student-Newman-Keuls and Scott-Knott are performed alternatively by bootstrap methods and show greater power and better controls of experimentwise type I error rates under non-normal, asymmetric, platykurtic or leptokurtic distributions.

Sign in / Sign up

Export Citation Format

Share Document