Making meaningful inferences about magnitudes

Area of Science:

• Biostatistics
• Clinical Research Methodology
• Data Interpretation

Background:

• Traditional statistical significance testing using P values can be misleading.
• Interpreting study outcomes requires understanding the magnitude of effects and associated uncertainty.

Purpose of the Study:

• To introduce a more intuitive and practical approach to statistical inference.
• To guide researchers in interpreting the real-world relevance of study findings.

Main Methods:

• Expressing uncertainty using confidence limits to define the likely range of the true value.
• Evaluating the practical significance by considering beneficial or harmful thresholds.
• Inferring outcome clarity based on whether the confidence interval overlaps positive and negative values.

Main Results:

• A framework for inferring whether the true value is substantially positive, trivial, or substantially negative.
• Qualitative likelihood statements regarding the true value's magnitude (e.g., 'very likely beneficial').
• Potential for quantitative or qualitative probabilities for various magnitude levels.

Conclusions:

• This approach offers a clearer interpretation of study results than traditional P value-based significance.
• It facilitates better decision-making regarding the utility and implications of research outcomes.