A Forum for Researchers

1977 ◽  
Vol 8 (1) ◽  
pp. 74-80
Author(s):  
Randy Ellsworth ◽  
Richard L. Isakson ◽  
Kenneth J. Travers
Keyword(s):  
Correlation Coefficient ◽  
Null Hypothesis ◽  
Type I Error ◽  
Type I ◽  
Original Experiment ◽  
Research Results ◽  
Replication Studies ◽  
Interest In Research ◽  
Rule Out

Eastman's (1975) recent expression in the JRME of concern over so few replication studies is a concern that should be shared by all who have an interest in research. Replication is critical in assessing the “significance” of research results. A correlation coefficient of .20, which is statistically significant at the .01 level, will be much more “significant” if we can demonstrate by replication that the same result occurs again and again. By replicating, we help rule out the possibility that a Type I error (we rejected the null hypothesis when it was true) occurred in the original experiment. Furthermore, by independently replicating with different subjects, at different times and places, we also are helping to increase the generalizability of any “significant” results we do obtain.

scholarly journals REMAXINT: a two-mode clustering-based method for statistical inference on two-way interaction

2021 ◽  
Author(s):  
Zaheer Ahmed ◽  
Alberto Cassese ◽  
Gerard van Breukelen ◽  
Jan Schepers
Keyword(s):  
Null Hypothesis ◽  
Type I Error ◽  
Type I ◽  
The Novel ◽  
Simulation Studies ◽  
Test Statistic ◽  
Clustering Model ◽  
Novel Method ◽  
Main Effects ◽  
Empirical Case Studies

AbstractWe present a novel method, REMAXINT, that captures the gist of two-way interaction in row by column (i.e., two-mode) data, with one observation per cell. REMAXINT is a probabilistic two-mode clustering model that yields two-mode partitions with maximal interaction between row and column clusters. For estimation of the parameters of REMAXINT, we maximize a conditional classification likelihood in which the random row (or column) main effects are conditioned out. For testing the null hypothesis of no interaction between row and column clusters, we propose a $$max-F$$ m a x - F test statistic and discuss its properties. We develop a Monte Carlo approach to obtain its sampling distribution under the null hypothesis. We evaluate the performance of the method through simulation studies. Specifically, for selected values of data size and (true) numbers of clusters, we obtain critical values of the $$max-F$$ m a x - F statistic, determine empirical Type I error rate of the proposed inferential procedure and study its power to reject the null hypothesis. Next, we show that the novel method is useful in a variety of applications by presenting two empirical case studies and end with some concluding remarks.

scholarly journals Considering Both Statistical and Clinical Significance

2016 ◽  
Vol 5 (5) ◽  
pp. 16 ◽  
Author(s):  
Guolong Zhao
Keyword(s):  
Clinical Significance ◽  
Null Hypothesis ◽  
Type I Error ◽  
Statistical Significance ◽  
Error Rates ◽  
Type I ◽  
Data Set ◽  
Type I Error Rates ◽  
Empirical Coverage ◽  
Superiority Trials

To evaluate a drug, statistical significance alone is insufficient and clinical significance is also necessary. This paper explains how to analyze clinical data with considering both statistical and clinical significance. The analysis is practiced by combining a confidence interval under null hypothesis with that under non-null hypothesis. The combination conveys one of the four possible results: (i) both significant, (ii) only significant in the former, (iii) only significant in the latter or (iv) neither significant. The four results constitute a quadripartite procedure. Corresponding tests are mentioned for describing Type I error rates and power. The empirical coverage is exhibited by Monte Carlo simulations. In superiority trials, the four results are interpreted as clinical superiority, statistical superiority, non-superiority and indeterminate respectively. The interpretation is opposite in inferiority trials. The combination poses a deflated Type I error rate, a decreased power and an increased sample size. The four results may helpful for a meticulous evaluation of drugs. Of these, non-superiority is another profile of equivalence and so it can also be used to interpret equivalence. This approach may prepare a convenience for interpreting discordant cases. Nevertheless, a larger data set is usually needed. An example is taken from a real trial in naturally acquired influenza.

scholarly journals Model Error in Covariance Structure Models

Methodology ◽  
2008 ◽  
Vol 4 (4) ◽  
pp. 159-167 ◽  
Author(s):  
Donna L. Coffman
Keyword(s):  
Error Rate ◽  
Null Hypothesis ◽  
Type I Error ◽  
Sampling Error ◽  
Model Error ◽  
Type I ◽  
Empirical Power ◽  
Parameter Drift ◽  
Type I Error Rate ◽  
Close Fit

This study investigated the degree to which violation of the parameter drift assumption affects the Type I error rate for the test of close fit and the power analysis procedures proposed by MacCallum et al. (1996) for both the test of close fit and the test of exact fit. The parameter drift assumption states that as sample size increases both sampling error and model error (i.e., the degree to which the model is an approximation in the population) decrease. Model error was introduced using a procedure proposed by Cudeck and Browne (1992). The empirical power for both the test of close fit, in which the null hypothesis specifies that the root mean square error of approximation (RMSEA) ≤ 0.05, and the test of exact fit, in which the null hypothesis specifies that RMSEA = 0, is compared with the theoretical power computed using the MacCallum et al. (1996) procedure. The empirical power and the theoretical power for both the test of close fit and the test of exact fit are nearly identical under violations of the assumption. The results also indicated that the test of close fit maintains the nominal Type I error rate under violations of the assumption.

When Studies are in Error: Basic Statistical Vocabulary Needed to Understand Clinical Studies

1996 ◽  
Vol 1 (1) ◽  
pp. 25-28 ◽  
Author(s):  
Martin A. Weinstock
Keyword(s):  
Null Hypothesis ◽  
Statistical Power ◽  
Critical Appraisal ◽  
Type I Error ◽  
Statistical Significance ◽  
P Value ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Error Type

Background: Accurate understanding of certain basic statistical terms and principles is key to critical appraisal of published literature. Objective: This review describes type I error, type II error, null hypothesis, p value, statistical significance, a, two-tailed and one-tailed tests, effect size, alternate hypothesis, statistical power, β, publication bias, confidence interval, standard error, and standard deviation, while including examples from reports of dermatologic studies. Conclusion: The application of the results of published studies to individual patients should be informed by an understanding of certain basic statistical concepts.

scholarly journals Three New Methods for Analysis of Answer Changes

2016 ◽  
Vol 77 (1) ◽  
pp. 54-81 ◽  
Author(s):  
Sandip Sinharay ◽  
Matthew S. Johnson
Keyword(s):  
Normal Distribution ◽  
Null Hypothesis ◽  
Type I Error ◽  
Error Rates ◽  
Standard Normal Distribution ◽  
Type I ◽  
Data Set ◽  
Continuity Correction ◽  
Type I Error Rates ◽  
Standard Normal

In a pioneering research article, Wollack and colleagues suggested the “erasure detection index” (EDI) to detect test tampering. The EDI can be used with or without a continuity correction and is assumed to follow the standard normal distribution under the null hypothesis of no test tampering. When used without a continuity correction, the EDI often has inflated Type I error rates. When used with a continuity correction, the EDI has satisfactory Type I error rates, but smaller power compared with the EDI without a continuity correction. This article suggests three methods for detecting test tampering that do not rely on the assumption of a standard normal distribution under the null hypothesis. It is demonstrated in a detailed simulation study that the performance of each suggested method is slightly better than that of the EDI. The EDI and the suggested methods were applied to a real data set. The suggested methods, although more computation intensive than the EDI, seem to be promising in detecting test tampering.

scholarly journals Improved procedures and computer programs for equivalence assessment of correlation coefficients

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252323
Author(s):  
Gwowen Shieh
Keyword(s):  
Correlation Coefficient ◽  
Type I Error ◽  
Reference Range ◽  
Correlation Coefficients ◽  
Sample Size Determination ◽  
Power Calculation ◽  
Type I ◽  
Significance Tests ◽  
Equivalence Tests ◽  
Essential Problem

The correlation coefficient is the most commonly used measure for summarizing the magnitude and direction of linear relationship between two response variables. Considerable literature has been devoted to the inference procedures for significance tests and confidence intervals of correlations. However, the essential problem of evaluating correlation equivalence has not been adequately examined. For the purpose of expanding the usefulness of correlational techniques, this article focuses on the Pearson product-moment correlation coefficient and the Fisher’s z transformation for developing equivalence procedures of correlation coefficients. Equivalence tests are proposed to assess whether a correlation coefficient is within a designated reference range for declaring equivalence decisions. The important aspects of Type I error rate, power calculation, and sample size determination are also considered. Special emphasis is given to clarify the nature and deficiency of the two one-sided tests for detecting a lack of association. The findings demonstrate the inappropriateness of existing methods for equivalence appraisal and validate the suggested techniques as reliable and primary tools in correlation analysis.

scholarly journals Első- és másodfajú etikai kudarcok (Type I and type II ethical errors)

2012 ◽  
pp. 56-63
Author(s):  
Zsuzsanna Győri
Keyword(s):  
Common Good ◽  
Null Hypothesis ◽  
Type I Error ◽  
Holistic Approach ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Opportunistic Behaviour ◽  
Self Interest ◽  
The Government

A cikkben a szerző a piac és a kormányzat kudarcaiból kiindulva azonosítja a közjó elérését célzó harmadik rendszer, az etikai felelősség kudarcait. Statisztikai analógiát használva elsőfajú kudarcként azonosítja, mikor az etikát nem veszik figyelembe, pedig szükség van rá. Ugyanakkor másodfajú kudarcként kezeli az etika profitnövelést célzó használatát, mely megtéveszti az érintetteteket, így még szélesebb utat enged az opportunista üzleti tevékenységnek. Meglátása szerint a három rendszer egymást nemcsak kiegészíti, de kölcsönösen korrigálja is. Ez az elsőfajú kudarc esetében általánosabb, a másodfajú kudarc megoldásához azonban a gazdasági élet alapvetéseinek átfogalmazására, az önérdek és az egydimenziós teljesítményértékelés helyett egy új, holisztikusabb szemléletű közgazdaságra van szükség. _______ In the article the author identifies the errors of ethical responsibility. That is the third system to attain common good, but have similar failures like the other two: the hands of the market and the government. Using statistical analogy the author identifies Type I error when ethics are not considered but it should be (null hypothesis is rejected however it’s true). She treats the usage of ethics to extend profit as Type II error. This misleads the stakeholders and makes room for opportunistic behaviour in business (null hypothesis is accepted in turn it’s false). In her opinion the three systems: the hand of the market, the government and the ethical management not only amend but interdependently correct each other. In the case of Type I error it is more general. Nevertheless to solve the Type II error we have to redefine the core principles of business. We need a more holistic approach in economics instead of self-interest and one-dimensional interpretation of value.

Comparing Group and Subgroup Cohesion Scores: A Nonparametric Method with an Application to Brazil

2003 ◽  
Vol 11 (3) ◽  
pp. 275-288 ◽  
Author(s):  
Scott W. Desposato
Keyword(s):  
Null Hypothesis ◽  
Type I Error ◽  
Statistical Properties ◽  
Legislative Behavior ◽  
Type I ◽  
Nonparametric Method ◽  
Roll Call ◽  
Legislative Studies ◽  
National Congress ◽  
Permutation Analysis

This article builds a nonparametric method for inference from roll-call cohesion scores. Cohesion scores have been a staple of legislative studies since the publication of Rice's 1924 thesis. Unfortunately, little effort has been dedicated to understanding their statistical properties or relating them to existing models of legislative behavior. I show how a common use of cohesion scores, testing for distinct voting blocs, is severely biased toward Type I error, practically guaranteeing significant findings even when the null hypothesis is correct. I offer a nonparametric method—permutation analysis—that solves the bias problem and provides for simple and intuitive inference. I demonstrate with an examination of roll-call voting data from the Brazilian National Congress.

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