Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors

Area of Science:

• Statistics
• Research Methodology
• Biostatistics

Background:

• Traditional statistical power analysis focuses narrowly on statistical significance.
• This emphasis can lead to misleading results, especially in small-sample or noisy research settings.
• Existing methods do not adequately address potential biases in effect size estimation.

Purpose of the Study:

• To introduce a novel design calculation framework for research studies.
• To address the limitations of conventional power analysis by incorporating Type S (sign) and Type M (magnitude) errors.
• To guide researchers in designing studies that yield more accurate and reliable effect size estimates.

Main Methods:

• Proposed design calculations estimate the probability of Type S error (wrong direction of effect).
• Proposed design calculations estimate the probability of Type M error (overestimation of effect magnitude, or exaggeration ratio).
• Illustrative examples from published research were used to demonstrate the application of these methods.

Main Results:

• The proposed methods provide a more comprehensive assessment of potential errors in study design compared to traditional power analysis.
• Estimating Type S and Type M errors helps researchers anticipate and mitigate biases in their findings.
• The primary challenge identified is obtaining accurate estimates of plausible effect sizes from external data.

Conclusions:

• Researchers should adopt design calculations that include Type S and Type M error probabilities.
• Accurate estimation of plausible effect sizes is crucial for effective design calculations.
• This approach enhances the reliability of research findings, particularly in challenging data environments.