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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

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Published on: September 7, 2019

Uncertainty limits the use of power analysis.

Jolynn Pek¹, Mark A Pitt¹, Duane T Wegener¹

¹Department of Psychology, The Ohio State University.

Journal of Experimental Psychology. General

|April 8, 2024

Summary

Statistical power calculations for experiment design can be misleading. Uncertainty in effect size estimates and random fluctuations in population effect size make power values unreliable for justifying sample size.

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Area of Science:

Statistics
Experimental Design
Psychology

Background:

Statistical power is commonly used to justify sample size in experimental design.
Classical power calculations often neglect crucial sources of uncertainty.
This can lead to a false sense of precision in design choices.

Purpose of the Study:

To investigate the impact of uncertainty on statistical power calculations.
To demonstrate the consequences of incorporating sampling variability and population effect size fluctuations.
To evaluate the reliability of power-based sample size justifications.

Main Methods:

Utilized simulation studies to model the effects of uncertainty.
Incorporated sampling variability in the estimation of effect size (Cohen's d).
Introduced random fluctuations in the population effect size.

Main Results:

Sampling variability in effect size estimates introduces substantial uncertainty in power and sample size determination.
Random fluctuations in population effect size can render calculated power values meaningless.
Simulations showed that calculated power values can be highly unstable and misleading.

Conclusions:

Researchers should place minimal confidence in power-based sample size justifications.
Classical power calculations that ignore uncertainty provide a false sense of precision.
Rethinking the reliance on traditional power analysis for experimental design is recommended.