Literature DB >> 29629616

Four simple ways to increase power without increasing the sample size.

Stanley E Lazic1.   

Abstract

Underpowered experiments have three problems: true effects are harder to detect, the true effects that are detected tend to have inflated effect sizes and as power decreases so does the probability that a statistically significant result represents a true effect. Many biology experiments are underpowered and recent calls to change the traditional 0.05 significance threshold to a more stringent value of 0.005 will further reduce the power of the average experiment. Increasing power by increasing the sample size is often the only option considered, but more samples increases costs, makes the experiment harder to conduct and is contrary to the 3Rs principles for animal research. We show how the design of an experiment and some analytical decisions can have a surprisingly large effect on power.

Keywords:  experimental design; power; reproducibility; sample size; statistics

Mesh:

Year:  2018        PMID: 29629616     DOI: 10.1177/0023677218767478

Source DB:  PubMed          Journal:  Lab Anim        ISSN: 0023-6772            Impact factor:   2.471


  14 in total

1.  Permutation tests are robust and powerful at 0.5% and 5% significance levels.

Authors:  Kimihiro Noguchi; Frank Konietschke; Fernando Marmolejo-Ramos; Markus Pauly
Journal:  Behav Res Methods       Date:  2021-05-28

2.  Testing the interhemispheric deficit theory of dyslexia using the visual half-field technique.

Authors:  A R Bradshaw; Dvm Bishop; Zvj Woodhead
Journal:  Q J Exp Psychol (Hove)       Date:  2020-01-10       Impact factor: 2.143

3.  Training in experimental design and statistics is essential: Response to Jordan.

Authors:  Stanley E Lazic; Charlie J Clarke-Williams; Marcus R Munafò
Journal:  PLoS Biol       Date:  2018-10-15       Impact factor: 8.029

4.  Improving transparency and scientific rigor in academic publishing.

Authors:  Eric M Prager; Karen E Chambers; Joshua L Plotkin; David L McArthur; Anita E Bandrowski; Nidhi Bansal; Maryann E Martone; Hadley C Bergstrom; Anton Bespalov; Chris Graf
Journal:  Brain Behav       Date:  2018-12-02       Impact factor: 2.708

5.  The psychology of experimental psychologists: Overcoming cognitive constraints to improve research: The 47th Sir Frederic Bartlett Lecture.

Authors:  Dorothy Vm Bishop
Journal:  Q J Exp Psychol (Hove)       Date:  2019-11-14       Impact factor: 2.143

6.  Improving transparency and scientific rigor in academic publishing.

Authors:  Eric M Prager; Karen E Chambers; Joshua L Plotkin; David L McArthur; Anita E Bandrowski; Nidhi Bansal; Maryann E Martone; Hadley C Bergstrom; Anton Bespalov; Chris Graf
Journal:  Cancer Rep (Hoboken)       Date:  2018-12-02

Review 7.  Combining Animal Welfare With Experimental Rigor to Improve Reproducibility in Behavioral Neuroscience.

Authors:  Cássio Morais Loss; Fernando Falkenburger Melleu; Karolina Domingues; Cilene Lino-de-Oliveira; Giordano Gubert Viola
Journal:  Front Behav Neurosci       Date:  2021-11-30       Impact factor: 3.558

8.  Quantifying the benefits of using decision models with response time and accuracy data.

Authors:  Tom Stafford; Angelo Pirrone; Mike Croucher; Anna Krystalli
Journal:  Behav Res Methods       Date:  2020-03-30

9.  Effect size, sample size and power of forced swim test assays in mice: Guidelines for investigators to optimize reproducibility.

Authors:  Neil R Smalheiser; Elena E Graetz; Zhou Yu; Jing Wang
Journal:  PLoS One       Date:  2021-02-24       Impact factor: 3.240

10.  Outcome unpredictability affects outcome-specific motivation to learn.

Authors:  Genisius Hartanto; Evan Livesey; Oren Griffiths; Harald Lachnit; Anna Thorwart
Journal:  Psychon Bull Rev       Date:  2021-05-04
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