Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Statistical Significance01:50

Statistical Significance

20.4K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
20.4K
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

4.5K
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
4.5K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

282
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
282
Significance Testing: Overview01:04

Significance Testing: Overview

3.8K
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
3.8K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

208
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
208
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

2.1K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
2.1K

You might also read

Related Articles

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

Sort by
Same author

Alpine songbirds at higher elevations are only raised with a slight delay and therefore under harsher environmental conditions.

Ecology and evolution·2024
Same author

Finding food in a changing world: Small-scale foraging habitat preferences of an insectivorous passerine in the Alps.

Ecology and evolution·2023
Same author

Why and how we should join the shift from significance testing to estimation.

Journal of evolutionary biology·2022
Same author

Research on registered report research.

Nature human behaviour·2021
Same author

Identifying occupancy model inadequacies: can residuals separately assess detection and presence?

Ecology·2019
Same author

Considerations for assessing model averaging of regression coefficients.

Ecological applications : a publication of the Ecological Society of America·2016
Same journal

Standardized surgical access to the porcine temporomandibular joint: Anatomical basis for translational research.

Laboratory animals·2026
Same journal

Development of animal use in experiments: a brief historical overview.

Laboratory animals·2026
Same journal

The reduction potential of diet board feeding-survival and clinical chemistry of group-housed Sprague Dawley rats in a 24-month study.

Laboratory animals·2026
Same journal

Advancing the 3Rs? Researchers' perspectives on institutional facilitation in Switzerland - Part 2: executive facilitation.

Laboratory animals·2026
Same journal

Recognition of pain and distress.

Laboratory animals·2026
Same journal

Strengthening animal welfare: monitoring humane endpoints in a rat model of mammary tumorigenesis undergoing a ladder resistance training protocol.

Laboratory animals·2026
See all related articles

Related Experiment Video

Updated: Sep 10, 2025

Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition
14:01

Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition

Published on: May 22, 2015

42.9K

Giving less power to statistical power.

Megan D Higgs1, Valentin Amrhein2

  • 1Critical Inference LLC, Bozeman, USA.

Laboratory Animals
|August 21, 2025
PubMed
Summary
This summary is machine-generated.

Sample size justification requires more than statistical power calculations. Researchers should create a quantitative backdrop to connect study outcomes with real-world implications, improving research design and interpretation.

Keywords:
Compatibility intervalalpha levelconfidence intervaldichotomaniaprecisionstatistical significance

More Related Videos

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing
06:58

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing

Published on: January 24, 2020

7.4K
Performing Permanent Distal Middle Cerebral with Common Carotid Artery Occlusion in Aged Rats to Study Cortical Ischemia with Sustained Disability
09:11

Performing Permanent Distal Middle Cerebral with Common Carotid Artery Occlusion in Aged Rats to Study Cortical Ischemia with Sustained Disability

Published on: February 23, 2016

22.1K

Related Experiment Videos

Last Updated: Sep 10, 2025

Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition
14:01

Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition

Published on: May 22, 2015

42.9K
Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing
06:58

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing

Published on: January 24, 2020

7.4K
Performing Permanent Distal Middle Cerebral with Common Carotid Artery Occlusion in Aged Rats to Study Cortical Ischemia with Sustained Disability
09:11

Performing Permanent Distal Middle Cerebral with Common Carotid Artery Occlusion in Aged Rats to Study Cortical Ischemia with Sustained Disability

Published on: February 23, 2016

22.1K

Area of Science:

  • Biostatistics
  • Research Methodology

Background:

  • Sample size justification is crucial in animal and clinical research due to ethical considerations.
  • Current reliance on statistical power calculations often uses simplistic methods and default values.
  • Over-reliance on power calculations overlooks opportunities to enhance research design and interpretation during the planning phase.

Purpose of the Study:

  • To propose an alternative approach to sample size justification beyond traditional statistical power calculations.
  • To introduce the concept of a 'quantitative backdrop' for a more robust study design.
  • To enhance the a priori consideration of research outcome interpretation and its real-life implications.

Main Methods:

  • Developing a 'quantitative backdrop' by explicitly linking ranges of possible research outcomes to their expected real-life implications.
  • Utilizing the quantitative backdrop to inform sample size investigations based on desired precision (interval width).
  • Shifting focus from desired statistical power to achieving a precision sufficient for distinguishing practically important effects.

Main Results:

  • The quantitative backdrop facilitates a priori considerations for interpreting potential study results, including interval representations.
  • This approach can inform traditional power analyses or guide sample size selection based on precision.
  • Sample size justification is reframed as a nuanced investigation of measurement, design, analysis, and interpretation challenges.

Conclusions:

  • Sample size justification should be a comprehensive a priori investigation, not merely a mathematical exercise.
  • Constructing a quantitative backdrop provides a practical foundation for addressing design and interpretation challenges.
  • Prioritizing precision over power in sample size calculations leads to more meaningful and interpretable research outcomes.