Null-Hypothesis Testing with the T-Table

2016 ◽  
pp. 19-23
Author(s):  
Ton J. Cleophas ◽  
Aeilko H. Zwinderman
Keyword(s):  
Hypothesis Testing ◽  
Null Hypothesis ◽  
Null Hypothesis Testing

Impact of Biases in the False-Positive Rate on Null Hypothesis Testing

Epilepsy ◽  
2011 ◽  
pp. 241-248
Author(s):  
Ralph Andrzejak ◽  
Daniel Chicharro ◽  
Florian Mormann
Keyword(s):  
Hypothesis Testing ◽  
False Positive ◽  
Null Hypothesis ◽  
False Positive Rate ◽  
Null Hypothesis Testing ◽  
Positive Rate

Null-Hypothesis Testing with Graphs

2016 ◽  
pp. 13-18
Author(s):  
Ton J. Cleophas ◽  
Aeilko H. Zwinderman
Keyword(s):  
Hypothesis Testing ◽  
Null Hypothesis ◽  
Null Hypothesis Testing

scholarly journals Interval-Based Hypothesis Testing and Its Applications to Economics and Finance

Econometrics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 21
Author(s):  
Jae H. Kim ◽  
Andrew P. Robinson
Keyword(s):  
Hypothesis Testing ◽  
Null Hypothesis ◽  
Medical Science ◽  
Point Of View ◽  
Economic Time Series ◽  
New Era ◽  
Null Hypothesis Testing ◽  
Point Null Hypothesis ◽  
Economic Time ◽  
Linear Restrictions

This paper presents a brief review of interval-based hypothesis testing, widely used in bio-statistics, medical science, and psychology, namely, tests for minimum-effect, equivalence, and non-inferiority. We present the methods in the contexts of a one-sample t-test and a test for linear restrictions in a regression. We present applications in testing for market efficiency, validity of asset-pricing models, and persistence of economic time series. We argue that, from the point of view of economics and finance, interval-based hypothesis testing provides more sensible inferential outcomes than those based on point-null hypothesis. We propose that interval-based tests be routinely employed in empirical research in business, as an alternative to point null hypothesis testing, especially in the new era of big data.

scholarly journals Do psychology students interpret null hypothesis significance testing critically?

2017 ◽  
Author(s):  
Ivan Flis
Keyword(s):  
Hypothesis Testing ◽  
Null Hypothesis ◽  
Statistical Significance ◽  
Psychology Students ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Graduate Studies ◽  
Null Hypothesis Testing ◽  
Level Of Understanding ◽  
Grade Average

The goal of the study was to descriptively analyze the understanding of null hypothesis significance testing among Croatian psychology students considering how it is usually understood in textbooks, which is subject to Bayesian and interpretative criticism. Also, the thesis represents a short overview of the discussions on the meaning of significance testing and how it is taught to students. There were 350 participants from undergraduate and graduate programs at five faculties in Croatia (Zagreb – Centre for Croatian Studies and Faculty of Humanities and Social Sciences, Rijeka, Zadar, Osijek). Another goal was to ascertain if the understanding of null hypothesis testing among psychology students can be predicted by their grades, attitudes and interests. The level of understanding of null hypothesis testing was measured by the Test of statistical significance misinterpretations (NHST test) (Oakes, 1986; Haller and Krauss, 2002). The attitudes toward null hypothesis significance testing were measured by a questionnaire that was constructed for this study. The grades were operationalized as the grade average of courses taken during undergraduate studies, and as a separate grade average of methodological courses taken during undergraduate and graduate studies. The students have shown limited understanding of null hypothesis testing – the percentage of correct answers in the NHST test was not higher than 56% for any of the six items. Croatian students have also shown less understanding on each item when compared to the German students in Haller and Krauss’s (2002) study. None of the variables – general grade average, average in the methodological courses, two variables measuring the attitude toward null hypothesis significance testing, failing at least one methodological course, and the variable of main interest in psychology – were predictive for the odds of answering the items in the NHST test correctly. The conclusion of the study is that education practices in teaching students the meaning and interpretation of null hypothesis significance testing have to be taken under consideration at Croatian psychology departments.

scholarly journals An introductory tutorial on Bayesian inference using a simple pedagogical tool

2017 ◽  
Author(s):  
Guillermo CAMPITELLI
Keyword(s):  
Probability Theory ◽  
Bayesian Inference ◽  
Hypothesis Testing ◽  
Null Hypothesis ◽  
Traditional Approach ◽  
Postgraduate Students ◽  
Data Table ◽  
Null Hypothesis Testing ◽  
Testing Approach ◽  
Hypothesis Testing Approach

This tutorial on Bayesian inference targets psychological researchers who are trained in the null hypothesis testing approach and use of SPSS software. There a number ofexcellent quality tutorials on Bayesian inference, but their problem is that, they assume mathematical knowledge that most psychological researchers do not possess. Thistutorial starts from the idea that Bayesian inference is not more difficult than the traditional approach, but before being introduced to probability theory notation is necessary for the newcomer to understand simple probability principles, which could be explained without mathematical formulas or probability notation. For this purpose in this tutorial I use a simple tool-the parameter-data table-to explain how probability theory can easily be used to make inferences in research. Then I compare the Bayesian and the null hypothesis testing approach using the same tool. Only after having introduced these principles I show the formulas and notations and explain how they relate to the parameter-data table. It is to be expected that this tutorial will increase the use of Bayesian inference by psychological researchers. Moreover, Bayesian researchers may use this tutorial to teach Bayesian inference to undergraduate or postgraduate students.

Statistical Conclusion Validity

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Hypothesis Testing ◽  
Null Hypothesis ◽  
Hypothesis Test ◽  
Model Misspecification ◽  
Internal Validity ◽  
Error Rates ◽  
P Value ◽  
Type I ◽  
Null Hypothesis Testing ◽  
Statistical Conclusion

Chapter 6 addresses the sub-category of internal validity defined by Shadish et al., as statistical conclusion validity, or “validity of inferences about the correlation (covariance) between treatment and outcome.” The common threats to statistical conclusion validity can arise, or become plausible through either model misspecification or through hypothesis testing. The risk of a serious model misspecification is inversely proportional to the length of the time series, for example, and so is the risk of mistating the Type I and Type II error rates. Threats to statistical conclusion validity arise from the classical and modern hybrid significance testing structures, the serious threats that weigh heavily in p-value tests are shown to be undefined in Beyesian tests. While the particularly vexing threats raised by modern null hypothesis testing are resolved through the elimination of the modern null hypothesis test, threats to statistical conclusion validity would inevitably persist and new threats would arise.

Possible Solution to Publication Bias Through Bayesian Statistics, Including Proper Null Hypothesis Testing

2015 ◽  
Vol 9 (4) ◽  
pp. 280-302 ◽  
Author(s):  
Elly A. Konijn ◽  
Rens van de Schoot ◽  
Sonja D. Winter ◽  
Christopher J. Ferguson
Keyword(s):  
Hypothesis Testing ◽  
Bayesian Statistics ◽  
Publication Bias ◽  
Null Hypothesis ◽  
Null Hypothesis Testing

scholarly journals What Constitutes Science and Scientific Evidence: Roles of Null Hypothesis Testing

2016 ◽  
Vol 77 (3) ◽  
pp. 475-488 ◽  
Author(s):  
Mark Chang
Keyword(s):  
Hypothesis Testing ◽  
Multiple Regression ◽  
Null Hypothesis ◽  
Scientific Discovery ◽  
Scientific Evidence ◽  
Fundamental Principle ◽  
Statistical Testing ◽  
Similarity Principle ◽  
Null Hypothesis Testing ◽  
Null Hypothesis Statistical Testing

We briefly discuss the philosophical basis of science, causality, and scientific evidence, by introducing the hidden but most fundamental principle of science: the similarity principle. The principle’s use in scientific discovery is illustrated with Simpson’s paradox and other examples. In discussing the value of null hypothesis statistical testing, the controversies in multiple regression, and multiplicity issues in statistics, we describe how these difficult issues should be handled based on our interpretation of the similarity principle.

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