scholarly journals Which statistical significance test best detects oncomiRNAs in cancer tissues? An exploratory analysis

Oncotarget ◽  
2016 ◽  
Vol 7 (51) ◽  
pp. 85613-85623 ◽  
Author(s):  
Wei Tang ◽  
Zhijun Liao ◽  
Quan Zou
Keyword(s):  
Statistical Significance ◽  
Significance Test ◽  
Exploratory Analysis ◽  
Statistical Significance Test ◽  
Cancer Tissues

What Happened in Hazelwood: Statistics, Employment Discrimination, and the 80% Rule

1984 ◽  
Vol 9 (1) ◽  
pp. 139-186 ◽  
Author(s):  
Paul Meier ◽  
Jerome Sacks ◽  
Sandy L. Zabell
Keyword(s):  
Employment Discrimination ◽  
Equal Employment Opportunity Commission ◽  
Statistical Significance ◽  
United States Supreme Court ◽  
The United States ◽  
Significance Test ◽  
Black Teachers ◽  
Prima Facie ◽  
Statistical Significance Test ◽  
The Difference

Tests of statistical significance have increasingly been used in employment discrimination cases since the Supreme Court's decision in Hazelwood. In that case, the United States Supreme Court ruled that “in a proper case” statistical evidence can suffice for a prima facie showing of employment discrimination. The Court also discussed the use of a binomial significance test to assess whether the difference between the proportion of black teachers employed by the Hazelwood School District and the proportion of black teachers in the relevant labor market was substantial enough to indicate discrimination. The Equal Employment Opportunity Commission has proposed a somewhat stricter standard for evaluating how substantial a difference must be to constitute evidence of discrimination. Under the so-called 80% rule promulgated by the EEOC, the difference must not only be statistically significant, but the hire rate for the allegedly discriminated group must also be less than 80% of the rate for the favored group. This article argues that a binomial statistical significance test standing alone is unsatisfactory for evaluating allegations of discrimination because many of the assumptions on which such tests are based are inapplicable to employment settings; the 80% rule is a more appropriate standard for evaluating whether a difference in hire rates should be treated as a prima facie showing of discrimination.

scholarly journals Statistical Significance Test for Neural Network Classification

2012 ◽  
Vol 11 (1) ◽  
pp. 64
Author(s):  
Sri Rezeki ◽  
Subanar Subanar ◽  
Suryo Guritno
Keyword(s):  
Neural Networks ◽  
Model Selection ◽  
Cross Validation ◽  
Statistical Significance ◽  
Wald Test ◽  
Significance Test ◽  
Nonparametric Models ◽  
Statistical Significance Test ◽  
Selection Strategies ◽  
Iris Data

Model selection in neural networks can be guided by statistical procedures, such as hypothesis tests, informationcriteria and cross validation. Taking a statistical perspective is especially important for nonparametric models likeneural networks, because the reason for applying them is the lack of knowledge about an adequate functionalform. Many researchers have developed model selection strategies for neural networks which are based onstatistical concepts. In this paper, we focused on the model evaluation by implementing statistical significancetest. We used Wald-test to evaluate the relevance of parameters in the networks for classification problem.Parameters with no significance influence on any of the network outputs have to be removed. In general, theresults show that Wald-test work properly to determine significance of each weight from the selected model. Anempirical study by using Iris data yields all parameters in the network are significance, except bias at the firstoutput neuron.

scholarly journals A Statistical Significance Test for Necessary Condition Analysis

2018 ◽  
Vol 23 (2) ◽  
pp. 385-395 ◽  
Author(s):  
Jan Dul ◽  
Erwin van der Laan ◽  
Roelof Kuik
Keyword(s):  
Effect Size ◽  
Permutation Test ◽  
Statistical Significance ◽  
Empty Space ◽  
Significance Test ◽  
Necessary Condition ◽  
Statistical Significance Test ◽  
Randomness Test ◽  
Statistical Development

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 for an outcome Y. NCA puts a ceiling on the data, representing the level of X that is necessary (but not sufficient) for a given level of Y. The empty space above the ceiling relative to the total empirical space characterizes the necessity effect size. We propose a statistical significance test that evaluates the evidence against the null hypothesis of an effect being due to chance. Such a randomness test helps protect researchers from making Type 1 errors and drawing false positive conclusions. The test is an “approximate permutation test.” The test is available in NCA software for R. We provide suggestions for further statistical development of NCA.

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