A note on Lindley's paradox

Test ◽  
2006 ◽  
Vol 15 (1) ◽  
pp. 125-139 ◽  
Author(s):  
C. Andy Tsao
Keyword(s):  
Lindley’S Paradox

Lindley's Paradox: Rejoinder

1982 ◽  
Vol 77 (378) ◽  
pp. 349 ◽  
Author(s):  
Glenn Shafer
Keyword(s):  
Lindley’S Paradox

scholarly journals Justify Your Alpha: A Primer on Two Practical Approaches

2021 ◽  
Author(s):  
Maximilian Maier ◽  
Daniel Lakens
Keyword(s):  
Statistical Power ◽  
Alternative Hypothesis ◽  
R Package ◽  
Error Rates ◽  
Shiny App ◽  
Alpha Level ◽  
Type 2 Error ◽  
Lindley’S Paradox

The default use of an alpha level of 0.05 is suboptimal for two reasons. First, decisions based on data can be made more efficiently by choosing an alpha level that minimizes the combined Type 1 and Type 2 error rate. Second, it is possible that in studies with very high statistical power p-values lower than the alpha level can be more likely when the null hypothesis is true, than when the alternative hypothesis is true (i.e., Lindley's paradox). This manuscript explains two approaches that can be used to justify a better choice of an alpha level than relying on the default threshold of 0.05. The first approach is based on the idea to either minimize or balance Type 1 and Type 2 error rates. The second approach lowers the alpha level as a function of the sample size to prevent Lindley's paradox. An R package and Shiny app are provided to perform the required calculations. Both approaches have their limitations (e.g., the challenge of specifying relative costs and priors), but can offer an improvement to current practices, especially when sample sizes are large. The use of alpha levels that have a better justification should improve statistical inferences and can increase the efficiency and informativeness of scientific research.

Lindley's Paradox: Comment

1982 ◽  
Vol 77 (378) ◽  
pp. 344 ◽  
Author(s):  
Bruce M. Hill
Keyword(s):  
Lindley’S Paradox

Lindley's Paradox: Comment

1982 ◽  
Vol 77 (378) ◽  
pp. 334 ◽  
Author(s):  
D. V. Lindley
Keyword(s):  
Lindley’S Paradox

scholarly journals The Support Interval

2018 ◽  
Author(s):  
Eric-Jan Wagenmakers ◽  
Quentin Frederik Gronau ◽  
Fabian Dablander ◽  
Alexander Etz
Keyword(s):  
Confidence Interval ◽  
Hypothesis Test ◽  
Credible Interval ◽  
Minimum Amount ◽  
Evidence Based ◽  
Support Interval ◽  
Bayesian Credible Interval ◽  
Parameter Values ◽  
Lindley’S Paradox ◽  
Bayesian Support

A frequentist confidence interval can be constructed by inverting a hypothesis test, such that the interval contains only parameter values that would not have been rejected by the test. We show how a similar definition can be employed to construct a Bayesian support interval. Consistent with Carnap’s theory of corroboration, the support interval contains only parameter values that receive at least some minimum amount of support from the data. The support interval is not subject to Lindley’s paradox and provides an evidence-based perspective on inference that differ from the belief-based perspective that forms the basis of the standard Bayesian credible interval.

scholarly journals Significance Tests for Binomial Experiments: Ordering the Sample Space by Bayes Factors and Using Adaptive Significance Levels for Decisions

2017 ◽  
Author(s):  
Carlos A. de B. Pereira ◽  
Adriano Polpo ◽  
Eduardo Y. Nakano
Keyword(s):  
Null Hypothesis ◽  
Bayes Factor ◽  
Adaptive Significance ◽  
Bayes Factors ◽  
P Value ◽  
Significance Tests ◽  
Close Link ◽  
Significance Levels ◽  
Level Of Significance ◽  
Lindley’S Paradox

The main objective of this paper is to find a close link between the adaptive level of significance, presented here, and the sample size. We, statisticians, know of the inconsistency, or paradox, in the current classical tests of significance that are based on p-value statistics that is compared to the canonical significance levels (10%, 5% and 1%): "Raise the sample to reject the null hypothesis" is the recommendation of some ill-advised scientists! This paper will show that it is possible to eliminate this problem of significance tests. The Bayesian Lindley's paradox – "increase the sample to accept the hypothesis" – also disappears. Obviously, we present here only the beginning of a possible prominent research. The intention is to extend its use to more complex applications such as survival analysis, reliability tests and other areas. The main tools used here are the Bayes Factor and the extended Neyman-Pearson Lemma.

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