Outcome (game theory)

Quantum mechanics

Statistical power

Engineering

Reliability engineering

Aerospace engineering

Statistics

Chromatography

Computer security

Range (aeronautics)

Power (physics)

Data mining

Computer science

Statistical analysis

Dentistry

Management science

Post hoc

Physics

Power analysis

Psychology

Mathematics

Post-hoc analysis

Data science

Reliability (semiconductor)

Sample size determination

Mathematical economics

Cryptography

Information retrieval

Medicine

Chemistry

Sample (material)

General Psychiatry

Power analysis is a key component of planning prospective studies such as clinical trials. However, some journals in biomedical and psychosocial sciences request power analysis for data already collected and analysed before accepting manuscripts for publication. Many have raised concerns about the conceptual basis for such post-hoc power analyses. More recently, Zhang et al showed by using simulation studies that such power analyses do not indicate true power for detecting statistical significance since post-hoc power estimates vary in the range of practical interests and can be very different from the true power. On the other hand, journals’ request for information about the reliability of statistical findings in a manuscript due to small sample sizes is justified since the sample size plays an important role in the reproducibility of statistical findings. The problem is the wording of the journals' request, as the current power analysis paradigm is not designed to address journals’ concerns about the reliability of the statistical findings. In this paper, we propose an alternate formulation of power analysis to provide a conceptually valid approach to the journals’ wrongly worded but practically significant concern.

Post-hoc power analysis: a conceptually valid approach for power based on observed study data