scholarly journals Average deviation for measuring variation in data in small samples (n < 5)

2017 ◽  
Author(s):  
Wenfa Ng
Keyword(s):  
Sample Size ◽  
Small Sample Size ◽  
Small Sample ◽  
Standard Normal Distribution ◽  
Average Deviation ◽  
Standard Normal ◽  
The Mean ◽  
The Impact ◽  
Outlier Measurement ◽  
Calculated Average

Good control of experiment variability is critical to experiment success, and many methods are available for quantifying variation in data. Popular methods for measuring variability in data typically uses a statistical distribution such as a standard normal distribution, but these distributions are designed for large sample size with n > 30. However, experiments typically generate less than 5 replicates (n < 5). Thus, the key requirement for the use of standard normal distribution is not satisfied, which bring forth the need for the development of alternative ways of quantifying the variation in collected data for small sample size. This abstract describes a new statistic, average deviation, that aims to quantify the variation of repeated measurements of a variable. By taking an average of the sum of the differences between the mean and all measurements, average deviation provides a better representation of the variation in data around a mean, while capturing the impact of significant deviation from the mean by individual measurement. However, division of the sum of deviation of all measurements from the mean by the sample size meant that the presence of outlier measurement may not be fully represented by the calculated average deviation. Thus, the new statistic is better used with a small sample size of less than 5, which helps reduce the extent in which an outlier’s influence on the average deviation would be diluted. In summary, for small sample size, average deviation better represents the deviation between each measurement and the mean compared to statistical distribution-based approaches such as standard error. However, desire to not dilute the impact of outlier measurement on the calculated average deviation meant that the new statistic is only suitable for sample size less than 5.

scholarly journals Average deviation for measuring variation in data in small samples (n < 5)

2017 ◽  
Author(s):  
Wenfa Ng
Keyword(s):  
Sample Size ◽  
Small Sample Size ◽  
Small Sample ◽  
Standard Normal Distribution ◽  
Average Deviation ◽  
Standard Normal ◽  
The Mean ◽  
The Impact ◽  
Outlier Measurement ◽  
Calculated Average

Good control of experiment variability is critical to experiment success, and many methods are available for quantifying variation in data. Popular methods for measuring variability in data typically uses a statistical distribution such as a standard normal distribution, but these distributions are designed for large sample size with n > 30. However, experiments typically generate less than 5 replicates (n < 5). Thus, the key requirement for the use of standard normal distribution is not satisfied, which bring forth the need for the development of alternative ways of quantifying the variation in collected data for small sample size. This abstract describes a new statistic, average deviation, that aims to quantify the variation of repeated measurements of a variable. By taking an average of the sum of the differences between the mean and all measurements, average deviation provides a better representation of the variation in data around a mean, while capturing the impact of significant deviation from the mean by individual measurement. However, division of the sum of deviation of all measurements from the mean by the sample size meant that the presence of outlier measurement may not be fully represented by the calculated average deviation. Thus, the new statistic is better used with a small sample size of less than 5, which helps reduce the extent in which an outlier’s influence on the average deviation would be diluted. In summary, for small sample size, average deviation better represents the deviation between each measurement and the mean compared to statistical distribution-based approaches such as standard error. However, desire to not dilute the impact of outlier measurement on the calculated average deviation meant that the new statistic is only suitable for sample size less than 5.

Stillbirth Rate and Weight at Birth of Quintuplets in Japan

1989 ◽  
Vol 38 (1-2) ◽  
pp. 65-69 ◽  
Author(s):  
Yoko Imaizumi
Keyword(s):  
Sample Size ◽  
Birth Order ◽  
Gestational Age ◽  
Size Effects ◽  
Maternal Age ◽  
Small Sample Size ◽  
Small Sample ◽  
Stillbirth Rate ◽  
The Mean

AbstractNation-wide data in Japan on births and prenatal deaths of 16 sets of quintuplets during 1974-1985 were analysed. Among the 16 sets, 3 sets were liveborn, 8 were stillborn, and 5 were mixed, with a stillbirth rate of 0.64 (51/80). Effects of sex, maternal age and birth order on the stillbirth rate were not considered because of the small sample size. Effects of gestational age and birthweight on stillbirth rate were also examined. The mean weight of the 40 quintuplet individuals was 1,048 g.

scholarly journals The impact of visual impairment on nutritional status: A systematic review

2018 ◽  
Vol 36 (1) ◽  
pp. 17-30 ◽  
Author(s):  
Nabila Jones ◽  
Hannah Bartlett
Keyword(s):  
Nutritional Status ◽  
Visual Impairment ◽  
Sample Size ◽  
Visually Impaired ◽  
Small Sample Size ◽  
Small Sample ◽  
Control Group ◽  
Visually Impaired People ◽  
Impaired People ◽  
The Impact

The aim of this review was to evaluate the literature that has investigated the impact of visual impairment on nutritional status. We identified relevant articles through a multi-staged systematic approach. Fourteen articles were identified as meeting the inclusion criteria. The sample size of the studies ranged from 9 to 761 participants. It was found that visual impairment significantly affects nutritional status. The studies reported that visually impaired people have an abnormal body mass index (BMI); a higher prevalence of obesity and malnutrition was reported. Visually impaired people find it difficult to shop for, eat, and prepare meals. Most studies had a small sample size, and some studies did not include a study control group for comparison. The limitations of these studies suggest that the findings are not conclusive enough to hold true for only those who are visually impaired. Further studies with a larger sample size are required with the aim of developing interventions.

scholarly journals The Impact of Improved Thermistor Calibration on the Expendable Bathythermograph Profile Data

2017 ◽  
Vol 34 (9) ◽  
pp. 1947-1961 ◽  
Author(s):  
Marlos Goes ◽  
Elizabeth Babcock ◽  
Francis Bringas ◽  
Peter Ortner ◽  
Gustavo Goni
Keyword(s):  
Small Sample Size ◽  
Heat Content ◽  
Small Sample ◽  
Profile Data ◽  
Expendable Bathythermograph ◽  
Different Types ◽  
Long Term Trends ◽  
The Mean ◽  
The Impact

AbstractExpendable bathythermograph (XBT) data provide one of the longest available records of upper-ocean temperature. However, temperature and depth biases in XBT data adversely affect estimates of long-term trends of ocean heat content and, to a lesser extent, estimates of volume and heat transport in the ocean. Several corrections have been proposed to overcome historical biases in XBT data, which rely on constantly monitoring these biases. This paper provides an analysis of data collected during three recent hydrographic cruises that utilized different types of probes, and examines methods to reduce temperature and depth biases by improving the thermistor calibration and reducing the mass variability of the XBT probes.The results obtained show that the use of individual thermistor calibration in XBT probes is the most effective calibration to decrease the thermal bias, improving the mean thermal bias to less than 0.02°C and its tolerance from 0.1° to 0.03°C. The temperature variance of probes with screened thermistors is significantly reduced by approximately 60% in comparison to standard probes. On the other hand, probes with a tighter weight tolerance did not show statistically significant reductions in the spread of depth biases, possibly because of the small sample size or the sensitivity of the depth accuracy to other causes affecting the analysis.

Role of an extended pathology exam in risk classification of testicular germ cell tumors (GCTs).

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e15032-e15032
Author(s):  
Mihai Vasile Marinca ◽  
Irina Draga Caruntu ◽  
Ludmila Liliac ◽  
Simona Eliza Giusca ◽  
Andreea Marinca ◽  
...  
Keyword(s):  
Sample Size ◽  
Small Sample Size ◽  
Germ Cell Tumors ◽  
Disease Free Survival ◽  
Developed Countries ◽  
Small Sample ◽  
P Value ◽  
Testicular Germ Cell ◽  
Therapeutic Decisions ◽  
The Impact

e15032 Background: The 1997 IGCCCG Consensus classification provides clinicians with enough information to efficiently choose between treatment options for most GCT patients. Nevertheless, therapy is ineffective in 5-10% of cases (even more in less developed countries), and about the same numbers experience severe side effects. This exploratory study aims to assess the impact of more rigorous and detailed pathology examination on improving the assignation of these patients to prognostic groups and, consequently, making optimal therapeutic decisions. Methods: Predefined features were reviewed on histology slides from 39 GCT patients followed-up for a median of 48.28 months. We designed a uniform pathology protocol, focused on identifying potential new prognostic factors. Categorical and continuous variables were quantified using light microscopy and computer-aided morphometry and, due to the small sample size, their statistical correlation was analyzed by exact tests and Spearman’s rho, respectively. Significant (2-sided p-value <0.05, under sample size reserve) coefficient values were entered in hierarchical cluster analysis (HCA). Results: Favorable IGCCCG group, presence of seminoma, glandular tissue pattern, presence and histoarchitecture of lymphocytic infiltrate associated better survival rates and lower risk of progression. Invasion of the epididymis and spermatic cord, presence of teratoma, choriocarcinoma and yolk-sac elements, papillary pattern and cell pleomorphism predicted poorer outcomes. HCA yielded 2 significantly distinct patient groups in terms of overall survival (p=0.018) and time to progression (p=0.080), but not disease-free survival (p=0.614). Conclusions: Quantification of tumor subtypes and other histology features of GCTs (e.g. necrosis, tissue patterns, inflammation) is feasible and, if standardized, may prove useful in optimal selection of risk groups, when performed by an experienced pathologist.

Reliability of Regression-Corrected Climate Forecasts

2014 ◽  
Vol 27 (9) ◽  
pp. 3393-3404 ◽  
Author(s):  
Michael K. Tippett ◽  
Timothy DelSole ◽  
Anthony G. Barnston
Keyword(s):  
Sample Size ◽  
Climate Model ◽  
Sampling Error ◽  
Small Sample Size ◽  
Small Sample ◽  
Climate Forecast ◽  
Regression Parameters ◽  
Climate Forecasts ◽  
Probability Forecasts ◽  
The Impact

Abstract Regression is often used to calibrate climate model forecasts with observations. Reliability is an aspect of forecast quality that refers to the degree of correspondence between forecast probabilities and observed frequencies of occurrence. While regression-corrected climate forecasts are reliable in principle, the estimated regression parameters used in practice are affected by sampling error. The low skill and small sample sizes typically encountered in climate prediction imply substantial sampling error in the estimated regression parameters. Here the reliability of regression-corrected climate forecasts is analyzed for the case of joint-Gaussian distributed ensemble forecasts and observations with regression parameters estimated by least squares. Hypothesis testing of the regression parameters provides direct information about the skill and reliability of the uncorrected ensemble-based probability forecasts. However, the regression-corrected probability forecasts with estimated parameters are systematically “overconfident” because sampling error causes a positive bias in the regression forecast signal variance, despite the fact that the estimates of the regression parameters are themselves unbiased. An analytical description of the reliability diagram of a generic regression-corrected climate forecast is derived and is shown to depend on sample size and population correlation skill, with small sample size and low skill being factors that increase overconfidence. The analytical reliability estimate is shown to capture the effect of sampling error in synthetic data experiments and in a 29-yr dataset of NOAA Climate Forecast System version 2 predictions of seasonal precipitation totals over the Americas. The impact of sampling error on the reliability of regression-corrected forecast has been previously unrecognized and affects all regression-based forecasts. The use of regression parameters estimated by shrinkage methods such as ridge regression substantially reduces overconfidence.

scholarly journals Patch Based Collaborative Representation with Gabor Feature and Measurement Matrix for Face Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Zhengyuan Xu ◽  
Yu Liu ◽  
Mingquan Ye ◽  
Lei Huang ◽  
Hao Yu ◽  
...  
Keyword(s):  
Face Recognition ◽  
Sample Size ◽  
Small Sample Size ◽  
Large Data ◽  
Linear Representation ◽  
Small Sample ◽  
Collaborative Representation ◽  
Measurement Matrix ◽  
Gabor Feature ◽  
The Impact

In recent years, sparse representation based classification (SRC) has emerged as a popular technique in face recognition. Traditional SRC focuses on the role of the l1-norm but ignores the impact of collaborative representation (CR), which employs all the training examples over all the classes to represent a test sample. Due to issues like expression, illumination, pose, and small sample size, face recognition still remains as a challenging problem. In this paper, we proposed a patch based collaborative representation method for face recognition via Gabor feature and measurement matrix. Using patch based collaborative representation, this method can solve the problem of the lack of accuracy for the linear representation of the small sample size. Compared with holistic features, the multiscale and multidirection Gabor feature shows more robustness. The usage of measurement matrix can reduce large data volume caused by Gabor feature. The experimental results on several popular face databases including Extended Yale B, CMU_PIE, and LFW indicated that the proposed method is more competitive in robustness and accuracy than conventional SR and CR based methods.

The Impact of Three Gross Irn Bru on an Out-Patient Clinic for Depressed Men

1992 ◽  
Vol 20 (1) ◽  
pp. 73-78
Author(s):  
Jacqueline M. Atkinson ◽  
Denise A. Coia
Keyword(s):  
Sample Size ◽  
Multidisciplinary Team ◽  
Small Sample Size ◽  
Small Sample ◽  
Patient Relationship ◽  
Methodological Issues ◽  
Clinical Behaviour ◽  
Methodological Problems ◽  
The Impact

Using an ABA design, the impact of the unexpected delivery of Irn Bru to an out-patient clinic for depressed men was investigated using the Montgomery-Åsberg scale for depression. A significant improvement in both behaviour and affect was seen immediately, some benefit still showing at one month follow-up. The effect of the procedure on the multidisciplinary team is also discussed. Some methodological issues, including small sample size are explored. Despite the methodological problems the serious element of the study points to the important impact of unexpected, non-therapeutic elements on clinical behaviour, possibly as a result of the challenge to the therapist-patient relationship.

scholarly journals Tools for Ergonomic Intervention: The Development and Analysis of a Cost Calculator

2020 ◽  
Vol 64 (1) ◽  
pp. 465-469
Author(s):  
Derek Stephens ◽  
Diana J. Schwerha
Keyword(s):  
Decision Making ◽  
Sample Size ◽  
Small Sample Size ◽  
Small Sample ◽  
Quality Data ◽  
Sample Sizes ◽  
System Usability ◽  
The Mean ◽  
Larger Sample ◽  
Ergonomic Intervention

The purpose of this study was to determine if safety professionals can use an ergonomic intervention costing calculator, which integrates performance and quality data into the costing matrix, to increase communication and better of decision making for the company. The sample size included 9 participants, which included four safety managers, four EHS managers, and one HR generalist. Results showed that all participants found the calculator very useful, well integrated, and it increased communication across the company. The mean System Usability Score (SUS) score was 82, which is rated as a perfectly acceptable software for use. Recommendations from this study include adding some additional features to the calculator, increasing awareness and availability of the calculator, and conducting further analysis using larger sample sizes. Limitations in this study include small sample size and limited interventions that were tested.

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