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.

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

Paper Rejected (p>0.05)

2011 ◽  
pp. 323-328
Author(s):  
Mark. D. Dunlop ◽  
Mark Baillie
Keyword(s):  
Null Hypothesis ◽  
Research Community ◽  
Position Paper ◽  
Statistical Testing ◽  
Testing Methods ◽  
P Values ◽  
Null Hypothesis Statistical Testing

Null-hypothesis statistical testing has been seriously criticised in other domains, to the extent of some advocating a complete ban on publishing p-values. This short position paper aims to introduce the argument to the mobile-HCI research community, who make extensive use of the controversial testing methods.

The probability of a robust inference for internal validity

2020 ◽  
pp. 004912412091492
Author(s):  
Tenglong Li ◽  
Ken Frank
Keyword(s):  
Observational Study ◽  
Causal Inference ◽  
Null Hypothesis ◽  
Statistical Power ◽  
Internal Validity ◽  
Bayesian Framework ◽  
Statistical Testing ◽  
Robust Inference ◽  
Step Procedure ◽  
Null Hypothesis Statistical Testing

The internal validity of observational study is often subject to debate. In this study, we define the counterfactuals as the unobserved sample and intend to quantify its relationship with the null hypothesis statistical testing (NHST). We propose the probability of a robust inference for internal validity, that is, the PIV, as a robustness index of causal inference. Formally, the PIV is the probability of rejecting the null hypothesis again based on both the observed sample and the counterfactuals, provided the same null hypothesis has already been rejected based on the observed sample. Under either frequentist or Bayesian framework, one can bound the PIV of an inference based on his bounded belief about the counterfactuals, which is often needed when the unconfoundedness assumption is dubious. The PIV is equivalent to statistical power when the NHST is thought to be based on both the observed sample and the counterfactuals. We summarize the process of evaluating internal validity with the PIV into a six-step procedure and illustrate it with an empirical example.

Null hypothesis statistical testing and the balance between positive and negative approaches

2004 ◽  
Vol 27 (3) ◽  
pp. 338-339
Author(s):  
Adam S. Goodie
Keyword(s):  
Social Cognition ◽  
Null Hypothesis ◽  
Statistical Testing ◽  
Null Hypothesis Statistical Testing

Several of Krueger & Funder's (K&F's) suggestions may promote more balanced social cognition research, but reconsidered null hypothesis statistical testing (NHST) is not one of them. Although NHST has primarily supported negative conclusions, this is simply because most conclusions have been negative. NHST can support positive, negative, and even balanced conclusions. Better NHST practices would benefit psychology, but would not alter the balance between positive and negative approaches.

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.

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