The Most Difference in Means: A Statistic for the Strength of Null and Near-Zero Results

@article{Corliss2022TheMD,
  title={The Most Difference in Means: A Statistic for the Strength of Null and Near-Zero Results},
  author={Bruce A. Corliss and Taylor R. Brown and Ting Zhang and Kevin A. Janes and Heman Shakeri and Philip E. Bourne},
  journal={SSRN Electronic Journal},
  year={2022}
}
: Statistical insignificance does not suggest the absence of effect, yet scientists must often use null results as evidence of negligible (near-zero) effect size to falsify scientific hypotheses. Doing so must assess a result’s null strength, defined as the evidence for a negligible effect size. Such an assessment would differentiate high null strength results that suggest a negligible effect size from low null strength results that suggest a broad range of potential effect sizes. We propose… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 18 REFERENCES

The Importance of Prior Sensitivity Analysis in Bayesian Statistics: Demonstrations Using an Interactive Shiny App

The goal is that novice users can follow the process outlined here and work within the interactive Shiny App to gain a deeper understanding of the role of prior distributions and the importance of a sensitivity analysis when implementing Bayesian methods.

Hypothesis Testing in the Bayesian Framework

The hypothesis testing in the Bayesian framework is introduced and its pros and cons are discussed and it is emphasized that hypothesis testing is not the only way to make inference and its value should not be overstated.

It (review)

This paper presents a comprehensive review of the state-of-the-art in structural battery composites research. Structural battery composites are a class of structural power composites aimed to provide

The reign of the p-value is over: what alternative analyses could we employ to fill the power vacuum?

A quick-and-easy guide to some simple yet powerful statistical options that augment or replace the p-value, and that are relatively straightforward to apply, to support biologists in adopting new approaches where they feel that thep-value alone is not doing their data justice.

An Introduction to Second-Generation p-Values

The importance of second generation p-values is illustrated using a dataset of 247,000 single-nucleotide polymorphisms, i.e., genetic markers that are potentially associated with prostate cancer, to illustrate the importance of these advances.

Moving to a World Beyond “p < 0.05”

Some of you exploring this special issue of The American Statistician might be wondering if it’s a scolding from pedantic statisticians lecturing you about what not to do with p-values, without

On p-Values and Bayes Factors

The p-value quantifies the discrepancy between the data and a null hypothesis of interest, usually the assumption of no difference or no effect. A Bayesian approach allows the calibration of p-values

On the Sensitivity of Bayes Factors to the Prior Distributions

The Bayes factoris a Bayesian statistician's tool for model selection. Bayes factors can be highly sensitive to the prior distributions used for the parameters of the models under consideration. We

On the Behrens-Fisher Problem: A Review

The Behrens-Fisher problem arises when one seeks to make inferences about the means of two normal populations without assuming the variances are equal. This paper presents a review of fundamental