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The Power of Interstimulus Interval for the Assessment of Temporal Processing in Rodents

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Published on: April 19, 2019

Gaining power in multiple testing of interval hypotheses via conditionalization.

Jules L Ellis¹, Jakub Pecanka², Jelle J Goeman²

¹Behavioral Science Institute, Radboud University Nijmegen, Postbus 9104, 6500 HE, Nijmegen, The Netherlands.

Biostatistics (Oxford, England)

|September 25, 2018

Summary

This study introduces a novel filtering procedure to enhance the power of multiple testing procedures (MTPs) for interval hypotheses. By filtering and scaling P-values, researchers can achieve significant gains in statistical power for hypothesis testing.

Keywords:

Conditionalized test False discovery rate Family-wise error rate Multiple testing One-sided tests Uniform conditional stochastic order

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Area of Science:

Statistics
Statistical Inference
Hypothesis Testing

Background:

P-values for interval hypotheses can be stochastically larger than uniform under the null.
Standard multiple testing procedures (MTPs) may lack power when testing interval hypotheses.

Purpose of the Study:

To introduce a novel procedure for enhancing the power of MTPs for interval hypotheses.
To address the issue of stochastically larger P-values in interval hypothesis testing.

Main Methods:

A new procedure involves filtering P-values above a threshold and scaling the remaining ones.
A chosen family-wise error rate (FWER) or false discovery rate (FDR) MTP is applied to the corrected P-values.
The general validity of the procedure is proven under P-value independence.

Main Results:

The filtering procedure can lead to considerable gains in statistical power.
Sufficient conditions for controlling the family-wise error rate (FWER) are formulated for the Bonferroni method.
The method is generally valid under independence of P-values.

Conclusions:

The proposed filtering and scaling method effectively improves the power of MTPs for interval hypotheses.
This approach offers a practical way to increase statistical power in relevant hypothesis testing scenarios.
The procedure provides a valuable tool for researchers dealing with interval hypotheses.