On the effect of sample size of the normal database on statistical power of single subject analysis

2008 ◽  
Vol 29 (9) ◽  
pp. 837 ◽  
Author(s):  
Ralph Buchert
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Normal Database ◽  
Single Subject ◽  
Subject Analysis

scholarly journals Statistical power: implications for planning MEG studies

2019 ◽  
Author(s):  
Maximilien Chaumon ◽  
Aina Puce ◽  
Nathalie George
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Single Subject ◽  
List Type ◽  
Group Level ◽  
Spatial Properties ◽  
Human Connectome Project ◽  
Neurophysiological Studies ◽  
Subject Variability ◽  
The Impact

AbstractStatistical power is key for robust, replicable science. Here, we systematically explored how numbers of trials and subjects affect statistical power in MEG sensor-level data. More specifically, we simulated “experiments” using the MEG resting-state dataset of the Human Connectome Project (HCP). We divided the data in two conditions, injected a dipolar source at a known anatomical location in the “signal condition”, but not in the “noise condition”, and detected significant differences at sensor level with classical paired t-tests across subjects. Group-level detectability of these simulated effects varied drastically with anatomical origin. We thus examined in detail which spatial properties of the sources affected detectability, looking specifically at the distance from closest sensor and orientation of the source, and at the variability of these parameters across subjects. In line with previous single-subject studies, we found that the most detectable effects originate from source locations that are closest to the sensors and oriented tangentially with respect to the head surface. In addition, cross-subject variability in orientation also affected group-level detectability, boosting detection in regions where this variability was small and hindering detection in regions where it was large. Incidentally, we observed a considerable covariation of source position, orientation, and their cross-subject variability in individual brain anatomical space, making it difficult to assess the impact of each of these variables independently of one another. We thus also performed simulations where we controlled spatial properties independently of individual anatomy. These additional simulations confirmed the strong impact of distance and orientation and further showed that orientation variability across subjects affects detectability, whereas position variability does not.Importantly, our study indicates that strict unequivocal recommendations as to the ideal number of trials and subjects for any experiment cannot be realistically provided for neurophysiological studies. Rather, it highlights the importance of considering the spatial constraints underlying expected sources of activity while designing experiments.HighlightsAdequate sample size (number of subjects and trials) is key to robust neuroscienceWe simulated evoked MEG experiments and examined sensor-level detectabilityStatistical power varied by source distance, orientation & between-subject variabilityConsider source detectability at sensor-level when designing MEG studiesSample size for MEG studies? Consider source with lowest expected statistical power

P1-188: Enrichment strategies for secondary prevention trials: The effect of restrictive inclusion criteria on statistical power and sample size

2008 ◽  
Vol 4 ◽  
pp. T263-T264
Author(s):  
Steven D. Edland ◽  
Linda K. McEvoy ◽  
Dominic Holland ◽  
John C. Roddey ◽  
Christine Fennema-Notestine ◽  
...  
Keyword(s):  
Secondary Prevention ◽  
Sample Size ◽  
Statistical Power ◽  
Inclusion Criteria ◽  
Prevention Trials

Statistical Power Analysis can Improve Fisheries Research and Management

1990 ◽  
Vol 47 (1) ◽  
pp. 2-15 ◽  
Author(s):  
Randall M. Peterman
Keyword(s):  
Sample Size ◽  
Power Analysis ◽  
Statistical Power ◽  
Small Sample Size ◽  
Industrial Effluent ◽  
Small Sample ◽  
Detectable Effect ◽  
Type Ii ◽  
Sampling Variability ◽  
Statistical Power Analysis

Ninety-eight percent of recently surveyed papers in fisheries and aquatic sciences that did not reject some null hypothesis (H0) failed to report β, the probability of making a type II error (not rejecting H0 when it should have been), or statistical power (1 – β). However, 52% of those papers drew conclusions as if H0 were true. A false H0 could have been missed because of a low-power experiment, caused by small sample size or large sampling variability. Costs of type II errors can be large (for example, for cases that fail to detect harmful effects of some industrial effluent or a significant effect of fishing on stock depletion). Past statistical power analyses show that abundance estimation techniques usually have high β and that only large effects are detectable. I review relationships among β, power, detectable effect size, sample size, and sampling variability. I show how statistical power analysis can help interpret past results and improve designs of future experiments, impact assessments, and management regulations. I make recommendations for researchers and decision makers, including routine application of power analysis, more cautious management, and reversal of the burden of proof to put it on industry, not management agencies.

scholarly journals Statistical Primer for Athletic Trainers: Understanding the Role of Statistical Power in Comparative Athletic Training Research

2018 ◽  
Vol 53 (7) ◽  
pp. 716-719
Author(s):  
Monica R. Lininger ◽  
Bryan L. Riemann
Keyword(s):  
Sample Size ◽  
Athletic Training ◽  
Treatment Effect ◽  
Statistical Power ◽  
Athletic Trainers ◽  
A Priori ◽  
Significant Treatment ◽  
Training Research ◽  
Α Level

Objective: To describe the concept of statistical power as related to comparative interventions and how various factors, including sample size, affect statistical power.Background: Having a sufficiently sized sample for a study is necessary for an investigation to demonstrate that an effective treatment is statistically superior. Many researchers fail to conduct and report a priori sample-size estimates, which then makes it difficult to interpret nonsignificant results and causes the clinician to question the planning of the research design.Description: Statistical power is the probability of statistically detecting a treatment effect when one truly exists. The α level, a measure of differences between groups, the variability of the data, and the sample size all affect statistical power.Recommendations: Authors should conduct and provide the results of a priori sample-size estimations in the literature. This will assist clinicians in determining whether the lack of a statistically significant treatment effect is due to an underpowered study or to a treatment's actually having no effect.

scholarly journals An N-Pact Factor for Clinical Psychological Research

2018 ◽  
Author(s):  
Kathleen Wade Reardon ◽  
Avante J Smack ◽  
Kathrin Herzhoff ◽  
Jennifer L Tackett
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Empirical Studies ◽  
Psychological Research ◽  
Medium Effect ◽  
Medium Effect Size ◽  
One Step ◽  
Hard To Reach Populations ◽  
Self Examination ◽  
Adequate Sample Size

Although an emphasis on adequate sample size and statistical power has a long history in clinical psychological science (Cohen, 1992), increased attention to the replicability of scientific findings has again turned attention to the importance of statistical power (Bakker, van Dijk, & Wicherts, 2012). These recent efforts have not yet circled back to modern clinical psychological research, despite the continued importance of sample size and power in producing a credible body of evidence. As one step in this process of scientific self-examination, the present study estimated an N-pact Factor (the statistical power of published empirical studies to detect typical effect sizes; Fraley & Vazire, 2014) in two leading clinical journals (the Journal of Abnormal Psychology; JAP, and the Journal of Consulting and Clinical Psychology; JCCP) for the years 2000, 2005, 2010, and 2015. Study sample size, as one proxy for statistical power, is a useful focus because it allows direct comparisons with other subfields and may highlight some of the core methodological differences between clinical and other areas (e.g., hard-to-reach populations, greater emphasis on correlational designs). We found that, across all years examined, the average median sample size in clinical research is 179 participants (175 for JAP and 182 for JCCP). The power to detect a small-medium effect size of .20 is just below 80% for both journals. Although the clinical N-pact factor was higher than that estimated for social psychology, the statistical power in clinical journals is still limited to detect many effects of interest to clinical psychologists, with little evidence of improvement in sample sizes over time.

scholarly journals Methodological Reporting Behavior, Sample Sizes, and Statistical Power in Studies of Event- Related Potentials: Barriers to Reproducibility and Replicability

2019 ◽  
Author(s):  
Peter E Clayson ◽  
Kaylie Amanda Carbine ◽  
Scott Baldwin ◽  
Michael J. Larson
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Event Related Potentials ◽  
Reporting Guidelines ◽  
Medium Effect ◽  
Sample Sizes ◽  
Reporting Behavior ◽  
Average Sample Size ◽  
Related Potentials ◽  
Average Sample

Methodological reporting guidelines for studies of event-related potentials (ERPs) were updated in Psychophysiology in 2014. These guidelines facilitate the communication of key methodological parameters (e.g., preprocessing steps). Failing to report key parameters represents a barrier to replication efforts, and difficultly with replicability increases in the presence of small sample sizes and low statistical power. We assessed whether guidelines are followed and estimated the average sample size and power in recent research. Reporting behavior, sample sizes, and statistical designs were coded for 150 randomly-sampled articles from five high-impact journals that frequently publish ERP research from 2011 to 2017. An average of 63% of guidelines were reported, and reporting behavior was similar across journals, suggesting that gaps in reporting is a shortcoming of the field rather than any specific journal. Publication of the guidelines paper had no impact on reporting behavior, suggesting that editors and peer reviewers are not enforcing these recommendations. The average sample size per group was 21. Statistical power was conservatively estimated as .72-.98 for a large effect size, .35-.73 for a medium effect, and .10-.18 for a small effect. These findings indicate that failing to report key guidelines is ubiquitous and that ERP studies are primarily powered to detect large effects. Such low power and insufficient following of reporting guidelines represent substantial barriers to replication efforts. The methodological transparency and replicability of studies can be improved by the open sharing of processing code and experimental tasks and by a priori sample size calculations to ensure adequately powered studies.

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