Statistical Significance Tests

In two studies, this thesis depicts the relationship between minority group status in the United States, perceived discrimination, and coping with stress. Past literature on coping and its types – problem-focused versus emotion-focused – is inconsistent in terms of differences between minority status groups and majority groups. It remains unknown whether or why Black Americans and lesbian or gay Americans may demonstrate coping patterns that differ from White Americans and heterosexual Americans, respectively. What is altogether absent from the literature is the possible mediating factor of perceived discrimination experienced by these minority groups. That is, differences in internal, stable coping processes that manage stress may have been molded by one’s experience with discrimination. Study 1 examines the relationship between race (Black versus White) and coping, mediated by perceived discrimination. Study 2 examines the relationship between sexual orientation (lesbian or gay versus heterosexual) and coping, mediated by perceived discrimination. Both studies confirm the thesis that minority group members exhibit maladaptive, emotion-focused coping more than majority group
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members – but that this difference is explained by the minority group members’ perceived discrimination. Historical and political relevance, social implications, and possible limitations in design and interpretation are discussed.

Während meines Studiums der Wirtschaftsinformatik an der Hochschule Harz hatte ich in den Jahren 2002 bis 2005 das große Glück, als Tutor für verschiedene Statistik-Vorlesungen von Dr. Walter Strube tätig werden zu dürfen. Zu Beginn meiner Tutorentätigkeit konnte ich eine über die Jahre von Tutor zu Tutor weiterentwickelte Aufgabensammlung in Form einer ,,Lose-Blatt-Sammlung" von meiner direkten Vorgängerin, Frau Weinert, übernehmen. Um den Studierenden die Arbeit mit den Übungsaufgaben zu erleichtern, habe ich damals für sämtliche Aufgaben Musterlösungen und Erläuterungen verfasst und den Teilnehmern meiner Tutorien in Form von Handouts zur Verfügung gestellt. In den vergangenen sieben Jahren bin ich immer wieder von Studierenden nach dieser Sammlung von Aufgaben mit Musterlösungen gefragt worden, so dass ich mich dazu entschlossen habe, sie noch einmal zu überarbeiten, mit einigen weiteren Erläuterungen zu versehen und über den GRIN-Verlag sowie verschiedene Webseiten als kostenfreien Download zur Verfügung zu stellen. Ich hoffe, dass sie so vielleicht auch für den einen oder anderen Studierenden außerhalb unserer Hochschule von Nutzen sein kann.

The study tests the reaction of Bahrain Bourse to 2014 annual financial results announcement. The study is based on 30 companies. The researcher used event study methodology. The behaviour of average abnormal returns (AARs) and cumulative average abnormal returns (CAARs) are examined for 30 days prior to and 31 days after the announcement of annual financial results. Runs test, sign test and t-test statistics on AARs are statistically not significant. However, t-values on CAARs are statistically significant. Therefore, we conclude that Bahrain Bourse is not efficient in the semi-strong form. The findings help regulators to initiate measures to ensure market efficiency.

Speed of stock price response is important because if response is slow, the informed and alert investors would exploit it to earn abnormal returns by outperforming the market. This implies that market is inefficient in the semi-strong form. The study tests the reaction Indian stock market reaction to June 2014 quarterly financial results announcement. The study is based on 98 companies. The researcher used event study methodology. The behaviour of average abnormal returns (AARs) and cumulative average abnormal returns (CAARs) are examined for 30 days prior to and 31 days after the announcement of quarterly financial results. Runs test, sign test and t-test statistics on AARs are statistically not significant. However, t-values on CAARs are statistically significant. Therefore, we conclude that Indian stock market is not efficient in the semi-strong form.

This paper raises concerns about the advantages of using statistical significance tests in research assessments as has recently been suggested in the debate about proper normalization procedures for citation indicators.
Statistical significance tests are highly controversial and numerous criticisms have been leveled against their use. Based on examples from articles by proponents of the use statistical significance tests in research assessments, we address some of the numerous problems with such tests. The issues specifically discussed are the ritual practice of such tests, their dichotomous application in decision making, the difference between statistical and substantive significance, the implausibility of most null hypotheses, the crucial assumption of randomness, as well as the utility of standard errors and confidence intervals for inferential purposes. We argue that applying statistical significance tests and mechanically adhering to their results is highly problematic and detrimental to critical thinking. We claim that the use of such tests do not provide any advantages in relation to citation indicators, interpretations of them, or the decision making processes based upon them. On the contrary their use may be harmful. Like many other critics, we generally believe that statistical significance tests are over- and misused in the social sciences including scientometrics and we encourage a reform on these matters.

http://www.iaees.org/publications/journals/nb/articles/2022-12(3)/confidence-intervals-fallacies-criticisms-solutions.pdf

For a long time, confidence interval theory is the basis of statistics, and confidence interval has been regarded as an important content of statistical analysis. Almost all statistical textbooks and statistical analysis software contain the contents of confidence intervals, which are used to estimate statistical parameters or parameters of mathematical models, and are an important part of many methods such as interval estimation, analysis of variance, and regression analysis, etc. They are recommended or required by the method guidelines of many reputable journals. So far, confidence interval theory and methods have been widely used in various scientific or engineering fields including life sciences, medicine, environmental science, chemistry, physics, and psychology. However, due to the fallacies or deficiencies of the confidence interval theory and methodology, it has caused a wide range of misuses, and has been criticized more and more in recent years. Some statisticians even suggest abandoning the confidence interval theory. To avoid the problems of classical confidence interval theory, one can use Bayesian credible intervals, use uncertainty methods, calculate confidence intervals by avoiding statistic significance tests, or use the Bootstrap credible interval method proposed by me, etc. In practice, for controlled experiments, multiple replicates or treatments should be designed; for observational experiments, multiple representative samples should be drawn, and even a single sample can be used if sufficient sample size is ensured. It is necessary to implement the whole process control for every procedures from sampling to statistical analysis. Cross-comparison and validation of confidence interval analysis results with other multi-source results should be conducted to obtain the most reliable conclusions. Finally, in addition to writing, publishing and adopting new statistical works and teaching materials as soon as possible, it is imperative to revise and distribute various statistical software in new editions based on new statistics for use.

Free download at:
http://www.iaees.org/publications/journals/ces/articles/2022-12(3)/p-value-based-statistical-significance-tests.pdf

The p-value is at the heart of statistical significance tests, a very important issue related to the role of statistical inference in advancing scientific discovery. Over the past few decades, p-value based statistical significance tests have been widely used in most statistics-related research papers, textbooks, and all statistical software around the world. Numerous scientists in various disciplines hold the p-value as the gold standard for statistical significance. However, in recent years, the p-value based statistical significance tests have been questioned unprecedentedly, mainly because the paradigm of significance tests is wrong, p-value is too sensitive, p-value is a dichotomous subjective index, and statistical significance is related to sample size, etc. Scientific research can only be falsified, not confirmed. p-value based statistical significance tests are one of the sources of false conclusions and research reproducibility crisis. For this reason, many statisticians advocate to abandon p-value based statistical significance tests and replace them with effect size, Bayesian methods, meta-analysis, etc. Scientific inference that combines statistical testing and multiple types of evidence is the basis for producing reliable conclusions. Reliable scientific inference requires appropriate experimental design, sampling design, and sample size; it also requires full control of the research process. For complex and time-varying problems, the network or systematic methods should be used instead of the reductionist methods to obtain and analyze data. To change the scientific research paradigm, the paradigm of multiple repeated experiments and multi-sample testing should be adopted, and multiple parties should verify each other to improve the authenticity and reproducibility of the results. In addition to writing, publishing and adopting new statistical monographs and textbooks, the most urgent task is to revise and distribute various statistical software in the new versions based on the new statistics for further use. Before the popularization of new statistics, what we can do is to improve data quality, strict p-value levels of statistical significance tests, use more reasonable analysis methods or testing standards, and combine statistical analysis and mechanism analysis, etc.

In psychology as elsewhere, the main statistical inference strategy to establish empirical effects is null-hypothesis significance testing (NHST). The recent failure to replicate allegedly well-established NHST-results, however, implies that such results lack sufficient statistical power, and thus feature unacceptably high error-rates. Using data-simulation to estimate the error-rates of NHST-results, we advocate the (RPS) as a superior methodology. RPS integrates Frequentist with Bayesian inference elements, and leads from a preliminary discovery against a (random) -hypothesis to a statistical -verification. Not only do RPS-results feature significantly lower error-rates than NHST-results, RPS also addresses key-deficits of a "pure" Frequentist and a standard Bayesian approach. In particular, RPS aggregates underpowered results safely. RPS therefore provides a tool to regain the trust the discipline had lost during the ongoing replicability-crisis.

Allelic effects at quantitative trait loci (QTL) between lineages are potentially informative for indicating the action of natural selection. The QTL Sign Test uses the number of + and - alleles observed in a QTL study to infer a history of selection. This test has been constructed to condition on the phenotypic difference between the two lines in question. By applying the test to QTL data simulated under selection, we demonstrate that conditioning on the phenotypic difference results in a loss of power to reject the neutral hypothesis and marked sensitivity to variation in locus effect magnitude.