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2 edition of A comparison of methods for combining tests of significance found in the catalog.

A comparison of methods for combining tests of significance

by William C. Louv

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  • 31 Currently reading

Published .
Written in English


Edition Notes

Statementby William C. Louv
The Physical Object
Paginationvii, 122 leaves :
Number of Pages122
ID Numbers
Open LibraryOL24444665M
OCLC/WorldCa6429717

  The test illustrated in Figure 2 is called the one-sample t-test. Figure 3: The t and normal distributions. (a) The t distribution has higher tails that take into account that most samples will. Combining functions include Fisher’s combination test and the minimum p-value. Using randomization-based tests, the increase in power can be remarkable when compared with a single test and Simes’s method. The versatility of the method is that it also applies when the number of covariates exceeds the number of observations.

  Many statistical methods have been used to analyze observational data and modeling output and to compare the similarities and differences between observational and modeling results in the atmospheric and oceanic sciences ().To quantify the similarities and differences, statistical significance tests are commonly used to judge, for example, whether or not the means of two time series (or data. Sensory Analysis Section 4 Dr. Bruce W. Zoecklein 4 Table 1. Outline of Sensory Difference and Preference Tests 1 Indicates the minimum number of tasters required for testing to achieve a statistically significant result (p≤).2 Figures denote minimum number of correct responses required out of the total number of responses to conclude the wines are significantly different (p≤) from.

For each set of motors, the means and standard deviations of the failure times were ob- served as follows: Mean Standard Deviation Test 1 Test 2 Test3 Test 4 Tests Classically, these various sources of information would be joined using a linear combination of the separate estimates weighted inversely. The fact that we use y for lower-tailed and y - 1 for upper-tailed is confusing, but is just the way = FALSE works (there is a logical reason why it works this way, but not a logic that applies to tests of significance). Comparison of Tests of Significance for One-Sample Location Problems. We have now considered four tests.


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A comparison of methods for combining tests of significance by William C. Louv Download PDF EPUB FB2

A comparison of methods for combining tests of significance [William C. Louv] on *FREE* shipping on qualifying offers. This is a reproduction of a book published before This book may have occasional imperfections such as missing or blurred pages.

A comparison of methods for combining tests of significance. by William C. Louv. Share your thoughts Complete your review. Tell readers what you thought by rating and reviewing this book. Rate it. Six methods are studied for combining p-values from independent tests into a new test of the combined methods—minimum (The Method of Statistics, Williams and Norgate, London, ), chi-square (2)(Statistical Methods for Research Workers, 4th Edition, Oliver and Boyd, London, ), normal (Magyar Tudományos Akadémia Matematikai Kutató Intezetenek Kozlemenyei Cited by: Statistical significance testing is a central technique for everyday empirical-quantitative work in media and communication research.

Its most common form, the null hypothesis significance test Author: Steffen Lepa. These methods are analogues to multiple regression analyses for effect sizes and provide estimates of the parameters of the model, large sample tests of significance, and an explicit test of the specification of the model.

Thus, it is possible to test whether a model adequately explains the observed variability in effect size estimates. Many published papers include large numbers of significance tests.

These may be difficult to interpret because if we go on testing long enough we will inevitably find something which is “significant.” We must beware of attaching too much importance to a lone significant result among a mass of non-significant ones. It may be the one in 20 which we expect by chance alone.

Significance testing is used as a substitute for the traditional comparison of predicted value and experimental result at the core of the scientific method. When theory is only capable of predicting the sign of a relationship, a directional (one-sided) hypothesis test can be configured so that only a statistically significant result supports.

Application to independent test statistics. Fisher's method combines extreme value probabilities from each test, commonly known as "p-values", into one test statistic (X 2) using the formula ∼ − ∑ = ⁡ (), where p i is the p-value for the i th hypothesis test.

When the p-values tend to be small, the test statistic X 2 will be large, which suggests that the null hypotheses are not true.

Significance Levels The significance level for a given hypothesis test is a value for which a P-value less than or equal to is considered statistically significant. Typical values for are, and These values correspond to the probability of observing such an extreme value by chance.

In the test score example above, the P-value isso the probability of observing such a. Well, statistical significance tests can help you with that. Not just newspaper claims, they have wide use cases in industrial, technological and scientific applications as well. Correlation Test and Introduction to p value.

Why is it used. To test the linear relationship between two continuous variables. THE PAIRED-COMPARISON METHOD AS A SIMPLE DIFFERENCE TEST R. McBRIDE, A. WATSON and B. COX CSIRO Division of Food Research, 52, North RydeSydney, Australia School of Food Technobgy, University of New South Wales, Australia Sydney lkchnical College, Sydney, Australia Accepted for Publication Decem ABSTRACT The paired-comparison method.

The test statistics underpinning several methods for combining p-values are special cases of generalized mean p-value (GMP), including the minimum (Bonferroni procedure), harmonic mean and geometric mean.

A key assumption influencing the practical performance of such methods concerns the dependence between p. A consumer sensory evaluation panel (n = 48) evaluated the wines using a paired comparison test in which a sparkling wine at CO2 concentrations of, or g CO2/L was compared.

Combining independent test statistics is common in biomedical research. One approach is to combine the p-values of one-sided tests using Fisher's method (Fisher, ), referred to here as the Fisher's combination test (FCT). It has optimal Bahadur efficiency (Little and Folks, ).

However, in general, it has a disadvantage in the. multiple comparison/testing problems you might want to solve. This makes the interrelationships between the methods clearer and makes it easier for you to decide which analysis is appropriate.

The topics covered in the book as well as the software tools that are used are shown in Table 1. Examples One feature of the book is that all of the. Using the Bootstrap Method for a Statistical Significance Test of Differences between Summary Histograms Combining information from several observed/simulated cloud systems requires combining these histograms into a summary histogram.

The statistical hypothesis tests can validate the qualitative comparison between two histograms given. Prism 5 let you choose the significance level (,or ). Whatever significance level you choose, it also displays asterisks to denote the smallest of those significance levels at which the comparison is statistically significant.

How to. Ignoring these correlations overstates the statistical significance. Meta-analysis methods for combining p-values were modified to adjust for correlation. One of these methods is elaborated in the context of a comparison between two treatments.

The form of the correlation adjustment depends upon the alternative hypothesis. Significance testing has a number of advantages for presenting the results of operational tests and for deciding whether to pass (defense) systems to full-rate production.

1 Significance testing is a long-standing method for assessing whether an estimated quantity is significantly different from an assumed quantity. Therefore, it has utility in evaluating whether the results of an operational.

Tests of Significance is an elementary introduction to significance testing, this paper provides a conceptual and logical basis for understanding these tests. We have the choice to choose a one-tailed or two tailed test: if we do not make any assumption as to whether the mean of set 2 is larger or smaller than that of set 1 then we should chose the 2-tailed test.

We see that the p value iswhich is much greater than (5% level of significance). The test tells us that there is inadequate evidence to reject the Null Hypothesis and that the.Multiple comparisons of means allow you to examine which means are different and to estimate by how much they are different.

You can assess the statistical significance of differences between means using a set of confidence intervals, a set of hypothesis tests or both.we discuss methods that combine features of two or more techniques.

Chapter 14 deals with many of the practical issues that must be faced before drawing causal inferences from comparative studies. Use of the Book The book is intended for students, researchers, and administrators who have had a course in statistics or the equivalent experience.