Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Power

Power

The concept of work involves force and displacement; meanwhile, the work-energy theorem relates the net work done on a body to the difference in its kinetic energy, calculated between two points on its trajectory. While none of these quantities or relations involves time explicitly, we know that the time available to accomplish work is often just as important as the amount of work itself. For example, sprinters in a race may have achieved the same velocity at the finish, therefore,...

Complex Power

Complex Power

Power engineers have introduced the concept of complex power to determine the cumulative effect of parallel loads. This idea plays a crucial role in power analysis because it encompasses all the details related to the power consumed by a specific load.
Complex power is defined as the multiplication of the voltage and the complex conjugate of the current. The magnitude of this power, known as apparent power, is measured in volt-amperes (VA). Notably, the angle of the complex power equates to the...

Pareto Chart

Pareto Chart

A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...

The Power Flow Problem and Solution

The Power Flow Problem and Solution

Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk, phase angle δk, real power Pk, and reactive power Qk. Two of these four variables are inputs, while the power...

Average Power

Average Power

In practical electrical applications, the concept of time-varying instantaneous power is not frequently utilized. Instead, focus shifts to the more practical quantity known as average power. Average power is determined by integrating the instantaneous power over a specified time period and subsequently dividing it by that duration.

Power in a Three-Phase Circuit

Power in a Three-Phase Circuit

Three-phase systems have two configurations: the wye and delta. A star configuration can be three or four wires; in a delta configuration, the components are connected in a closed loop. Instantaneous power refers to the power value at a precise moment, and in a balanced three-phase system, it is constant. This is because the sum of the instantaneous powers in the three phases remains steady over time, despite individual fluctuations, due to the symmetry and phase relationship. The total...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Robust Bayesian multilevel meta-analysis: Adjusting for publication bias in the presence of dependent effect sizes.

Behavior research methods·2026

Same author

Assessing qualitative individual differences with Bayesian hierarchical latent-mixture models.

Psychological methods·2026

Same author

Random-effects psychophysics for studying individual differences in perception and cognition.

Psychonomic bulletin & review·2026

Same author

Investigating the replicability of the social and behavioural sciences.

Nature·2026

Same author

Investigating the analytical robustness of the social and behavioural sciences.

Nature·2026

Same author

Estimating correlations across tasks in experimental psychology.

Behavior research methods·2026

Same journal

Planned missingness in intensive longitudinal studies: Extensions and comparisons of multiform designs.

Behavior research methods·2026

Same journal

A validity-guided workflow for robust large language model research in psychology.

Behavior research methods·2026

Same journal

Are 7-point Likert scales preferable to 5-point scales in language research?

Behavior research methods·2026

Same journal

Generative psychometrics via AI-GENIE: Automatic item generation and validation with network-integrated evaluation.

Behavior research methods·2026

Same journal

Exploring psychological tradeoffs: Developing and demonstrating an R Shiny app for Pareto optimization.

Behavior research methods·2026

Same journal

The performance of Bayesian fit measures in detecting misspecified multilevel structural equation modeling.

Behavior research methods·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 23, 2026

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

A power fallacy.

Eric-Jan Wagenmakers¹, Josine Verhagen², Alexander Ly²

¹Department of Psychology, University of Amsterdam, Weesperplein 4, 1018 XA, Amsterdam, The Netherlands. ej.wagenmakers@gmail.com.

Behavior Research Methods

|October 2, 2014

Summary

This summary is machine-generated.

The power fallacy wrongly assumes high-power experiments are always more informative. This study shows low-power experiments can yield crucial insights, unlike high-power ones, challenging traditional statistical inference.

Keywords:

Bayes factor Hypothesis test Likelihood ratio Statistical evidence

More Related Videos

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Published on: August 9, 2016

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

Related Experiment Videos

Last Updated: Apr 23, 2026

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Published on: August 9, 2016

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

Area of Science:

Statistics
Scientific Methodology

Background:

The power fallacy is a common misconception in statistical inference.
It incorrectly assumes that high-power experiments consistently provide more informative results than low-power experiments.

Purpose of the Study:

To expose the power fallacy with concrete examples.
To demonstrate that experimental outcomes, not just power, determine informativeness.
To recommend alternative inference methods for observed data.

Main Methods:

Illustrative examples of high- and low-power experiments.
Comparison of inference from observed data versus hypothetical replications.
Introduction of likelihood ratios and Bayes factors for data-driven inference.

Main Results:

High-power experiment outcomes can be uninformative.
Low-power experiment outcomes can be highly informative.
Power is less useful and potentially misleading for inferring from observed data.

Conclusions:

The power fallacy is a critical misconception in scientific reasoning.
Likelihood ratios and Bayes factors offer a more rational approach to quantify evidence from observed data.
These methods condition on actual data, avoiding misleading averages over hypothetical experiments.