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Unicorns do exist: a tutorial on "proving" the null hypothesis.

David L Streiner¹

¹Kunin-Lunenfeld Applied Research Unit, Baycrest Centre for Geriatric Care, Toronto, Ontario. dstreiner@klaru-baycrest.on.ca

Canadian Journal of Psychiatry. Revue Canadienne De Psychiatrie

|January 22, 2004

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This study explains noninferiority testing, which proves a new treatment is not worse than an established one. It details how to reverse hypothesis roles and calculate sample sizes for such trials.

Area of Science:

Biostatistics
Clinical Trials

Background:

Traditional statistics focus on rejecting the null hypothesis.
Proving non-existence of a difference is crucial in clinical research, especially for new treatments.

Purpose of the Study:

To outline the logic of noninferiority testing.
To demonstrate how to calculate sample sizes for noninferiority studies.

Main Methods:

Differentiating between the null and "nill" hypotheses.
Reversing the roles of null and alternate hypotheses.
Defining an equivalence interval for group comparisons.

Main Results:

Explains the statistical framework for noninferiority.
Provides a method for sample size calculation in noninferiority trials.

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Conclusions:

Noninferiority testing allows demonstration that a new intervention is not worse than a standard.
This approach is vital for advancing medical treatments and requires specific statistical methodologies.