scholarly journals Not so different in present attitudes and behaviour, but expected future membership: A technical replication of a study of party youth in six European democracies

2018 ◽  
Vol 5 (1) ◽  
pp. 205316801876487
Author(s):  
Lion Behrens ◽  
Ingo Rohlfing
Keyword(s):  
Multiple Testing ◽  
Type I Error ◽  
Statistical Significance ◽  
Original Study ◽  
Pairwise Comparisons ◽  
Type I ◽  
Significance Tests ◽  
Party Members ◽  
Original Survey ◽  
Technical Replication

Based on the statistical analysis of an original survey of young party members from six European democracies, a study concluded that three types of young members differed systematically regarding their membership objectives, activism, efficacy and perceptions of the party and self-perceived political future. We performed a technical replication of the original study, correcting four deficiencies, which led us to a different conclusion. First, we discuss substantive significance in addition to statistical significance. Second, we ran significance tests on all comparisons instead of limiting them to an arbitrary subset. Third, we performed pairwise comparisons between the three types of members instead of using pooled groups. Fourth, we avoided the inflation of the type-I error rate due to multiple testing by using the Bonferroni–Holm correction. We found that most of the differences between the types lacked substantive significance, and that statistical significance only coherently distinguished the types of members in their future membership, but not in their present behaviour and attitudes.

scholarly journals Linkage Disequilibrium in Domestic Sheep

Genetics ◽  
2002 ◽  
Vol 160 (3) ◽  
pp. 1113-1122
Author(s):  
A F McRae ◽  
J C McEwan ◽  
K G Dodds ◽  
T Wilson ◽  
A M Crawford ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Type I Error ◽  
Statistical Significance ◽  
Domestic Sheep ◽  
Type I ◽  
Single Nucleotide ◽  
Microsatellite Genotype ◽  
Marker Distance ◽  
The Relationship ◽  
Two Populations

Abstract The last decade has seen a dramatic increase in the number of livestock QTL mapping studies. The next challenge awaiting livestock geneticists is to determine the actual genes responsible for variation of economically important traits. With the advent of high density single nucleotide polymorphism (SNP) maps, it may be possible to fine map genes by exploiting linkage disequilibrium between genes of interest and adjacent markers. However, the extent of linkage disequilibrium (LD) is generally unknown for livestock populations. In this article microsatellite genotype data are used to assess the extent of LD in two populations of domestic sheep. High levels of LD were found to extend for tens of centimorgans and declined as a function of marker distance. However, LD was also frequently observed between unlinked markers. The prospects for LD mapping in livestock appear encouraging provided that type I error can be minimized. Properties of the multiallelic LD coefficient D′ were also explored. D′ was found to be significantly related to marker heterozygosity, although the relationship did not appear to unduly influence the overall conclusions. Of potentially greater concern was the observation that D′ may be skewed when rare alleles are present. It is recommended that the statistical significance of LD is used in conjunction with coefficients such as D′ to determine the true extent of LD.

Analyzing Group Data

2021 ◽  
pp. 121-142
Author(s):  
Charles Auerbach
Keyword(s):  
Control Charts ◽  
Type I Error ◽  
Statistical Significance ◽  
Data Sets ◽  
Type I ◽  
Chi Square ◽  
Statistical Process ◽  
Incorrect Decision ◽  
Group Data ◽  
Statistical Process Control Charts

This chapter covers tests of statistical significance that can be used to compare data across phases. These are used to determine whether observed outcomes are likely the result of an intervention or, more likely, the result of chance. The purpose of a statistical test is to determine how likely it is that the analyst is making an incorrect decision by rejecting the null hypothesis and accepting the alternative one. A number of tests of significance are presented in this chapter: statistical process control charts (SPCs), proportion/frequency, chi-square, the conservative dual criteria (CDC), robust conservative dual criteria (RCDC), the t test, and analysis of variance (ANOVA). How and when to use each of these are also discussed. The method for transforming autocorrelated data and merging data sets is discussed. Once new data sets are created using the Append() function, they can be tested for Type I error using the techniques discussed in the chapter.

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 Generalized additive models and inflated type I error rates of smoother significance tests

2011 ◽  
Vol 55 (1) ◽  
pp. 366-374 ◽  
Author(s):  
Robin L. Young ◽  
Janice Weinberg ◽  
Verónica Vieira ◽  
Al Ozonoff ◽  
Thomas F. Webster
Keyword(s):  
Type I Error ◽  
Generalized Additive Models ◽  
Error Rates ◽  
Additive Models ◽  
Type I ◽  
Significance Tests ◽  
Type I Error Rates ◽  
Inflated Type

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.

The Conundrum of P-Values: Statistical Significance is Unavoidable but Need Medical Significance Too

2020 ◽  
Author(s):  
Abhaya Indrayan
Keyword(s):  
Type I Error ◽  
Dominant Role ◽  
Statistical Significance ◽  
Empirical Studies ◽  
P Value ◽  
Selective Reporting ◽  
Type I ◽  
Practical Application ◽  
P Values ◽  
Zero Effect

Background: Small P-values have been conventionally considered as evidence to reject a null hypothesis in empirical studies. However, there is widespread criticism of P-values now and the threshold we use for statistical significance is questioned.Methods: This communication is on contrarian view and explains why P-value and its threshold are still useful for ruling out sampling fluctuation as a source of the findings.Results: The problem is not with P-values themselves but it is with their misuse, abuse, and over-use, including the dominant role they have assumed in empirical results. False results may be mostly because of errors in design, invalid data, inadequate analysis, inappropriate interpretation, accumulation of Type-I error, and selective reporting, and not because of P-values per se.Conclusion: A threshold of P-values such as 0.05 for statistical significance is helpful in making a binary inference for practical application of the result. However, a lower threshold can be suggested to reduce the chance of false results. Also, the emphasis should be on detecting a medically significant effect and not zero effect.

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.

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