Flow-through imaging and automated analysis of oil-exposed early stage Atlantic cod ( Gadus morhua )
0
Citation
22
Reference
10
Related Paper
Abstract:
Toxicology studies in early fish life stages serve an important function in measuring the impact of potentially harmful substances, such as crude oil, on marine life. Morphometric analysis of larvae can reveal the effects of such substances in retarding growth and development. These studies are labor intensive and time consuming, typically resulting in only a small number of samples being considered. An automated system for imaging and measurement of experimental animals, using flow-through imaging and an artificial neural network to allow faster sampling of more individuals, has been described previously and used in toxicity experiments. This study compares the performance of the automated imaging and analysis system with traditional microscopy techniques in measuring biologically relevant endpoints using two oil treatments as positive controls. We demonstrate that while the automated system typically underestimates morphometric measurements relative to analysis of manual microscopy images, it shows similar statistical results to the manual method when comparing treatments across most endpoints. It allows for many more individual specimens to be sampled in a shorter time period, reducing labor requirements and improving statistical power in such studies, and is noninvasive allowing for repeated sampling of the same population.Keywords:
Automated method
Statistical power
Statistical Analysis
Statistical Power Analysis: Implications for Researchers, Planners, and Practitioners in Gerontology
This paper reports on the method of statistical power analysis and details procedures by which the research planner may design a more efficient experimental study. Attitude research published in the Gerontologist and the journal of Gerontology was submitted to a statistical power analysis. High numerical values were found, and compared and contrasted with those of past power-analytic investigations, the majority of which found low statistical power in their respective disciplines.
Statistical Analysis
Statistical power
Planner
Statistical theory
Power analysis
Cite
Citations (17)
Human performance and physiology: A statistical power analysis of ELF electromagnetic field research
Research examining the effects of electromagnetic fields (EMFs) on human performance and physiology has produced inconsistent results; this might be attributable to low statistical power. Statistical power refers to the probability of obtaining a statistically significant result, given the fact that a real effect exists. The results of a survey of published investigations of the effects of EMFs on human performance and physiology show that statistical power levels are very low, ranging from a mean of.08 for small effect sizes to .46 for large effect sizes. Implications of these findings for the interpretation of results are discussed along with suggestions for increasing statistical power. © 1996 Wiley-Liss, Inc.
Statistical power
Statistical Analysis
Cite
Citations (15)
The statistical analysis procedures used by adult education researchers have become more sophisticated over the last decade. However, critics have charged that researchers have failed to examine the technical adequacy of their work. In this study, the methodological soundness of adult education research was examined through a power analysis of the statistical tests used in volumes 21-32 of Adult Education. For each of the 1666 statistical tests reported, power estimates were obtained for three levels of hypothesized effect size. The results indicate that the power of statistical tests appearing in the adult education research literature is low when researchers are interested in finding small and medium effects. Many studies are being conducted with little chance of making a correct rejection of the null hypothesis. These findings are consistent with results obtained from similar analyses conducted in related fields, and point to ways in which researchers can increase the power of their statistical tests.
Statistical power
Soundness
Statistical Analysis
Cite
Citations (4)
Despite recommendations from the Publication Manual of the American Psychological Association (6th ed.) to include information on statistical power when publishing quantitative results, authors seldom include analysis or discussion of statistical power. The rationale for discussing statistical power is addressed, approaches to using G*Power to report statistical power are presented, and examples for reporting statistical power are provided.
Statistical power
Statistical Analysis
Cite
Citations (117)
Statistical power
Statistical Inference
Statistical Analysis
Statistical theory
Nomogram
Cite
Citations (41)
This study was suggested by the frequent encounters of the authors with beginning researchers who tend to avoid the more powerful research designs and statistical procedures such as the Solomon Four-Group design and analysis of covariance. The combination of an effective design and a powerful statistical analysis technique resulted in a significant finding for only 44 learning disabled students. Researchers are encouraged to seek pre-existing or easily obtainable covariates to incorporate into the statistical treatment whenever added power is required.
Power analysis
Statistical Analysis
Analysis of covariance
Statistical power
Research Design
Cite
Citations (5)
Cracks and voids are common defects in rotating systems and are a precursor to fatigue-induced failure. The application of statistical analysis, as a tool for damage identification and health monitoring in rotating machinery, is investigated. Experimental vibration data were collected for a set of health and cracked shafts. Formal statistical models have been proposed to describe the relationship between the vibration signals and the existence of damage. Damage detection and diagnosis are implemented based on statistical estimation and hypothesis testing. Such a statistical model provides a screening technique to detect other damage types. As a result, the proposed methods can improve the power of damage detection.
Statistical Analysis
Statistical power
Identification
Condition Monitoring
Cite
Citations (0)
Statistical power is an important factor for researchers to consider when designing and conducting quantitative studies. Lack of statistical power may lead to erroneous conclusions. To boost statistical power, however, may entail a larger sample size or better design to detect an effect size. This entry discusses the nature of statistical power, and further introduces basic procedures and caveats for conducting power analysis.
Statistical Analysis
Statistical power
Power analysis
Sample (material)
Cite
Citations (0)
The aim is of this study was to show the poor statistical power of postmortem studies. Further, this study aimed to find an estimate of the effect size for postmortem studies in order to show the importance of this parameter. This can be an aid in performing power analysis to determine a minimal sample size.GPower was used to perform calculations on sample size, effect size, and statistical power. The minimal significance (α) and statistical power (1 - β) were set at 0.05 and 0.80 respectively. Calculations were performed for two groups (Student's t-distribution) and multiple groups (one-way ANOVA; F-distribution).In this study, an average effect size of 0.46 was found (n = 22; SD = 0.30). Using this value to calculate the statistical power of another group of postmortem studies (n = 5) revealed that the average statistical power of these studies was poor (1 - β < 0.80).The probability of a type-II error in postmortem studies is considerable. In order to enhance statistical power of postmortem studies, power analysis should be performed in which the effect size found in this study can be used as a guideline.
Statistical power
Statistical Analysis
Power analysis
Cite
Citations (21)
In the use of statistical tests of hypotheses, the probability of correctly rejecting the null hypothesis is known as statistical power. A study designed with low statistical power is a study designed with a high probability of failure. The study discussed in this article examined the statistical power of articles published in Social Work Research and Abstracts from 1977 through 1984. The results indicated that almost halt the reported studies could not detect a medium effect size with adequate statistical power.
Statistical power
Statistical Analysis
Statistical theory
Null (SQL)
Statistical evidence
Cite
Citations (26)