Sampling Variability and Life History Features: Basic Considerations in the Design of Aquatic Insect Studies

1979 ◽  
Vol 36 (3) ◽  
pp. 290-311 ◽  
Author(s):  
Vincent H. Resh

Sampling variability in benthic studies may result from sampling device operation, physical features of the environment, laboratory sorting procedures, and biological features of study populations. Selected factors and procedures that influence variability, samplers affected, and proposed remedies are presented. Consequences of not considering autecological components in sampling designs are illustrated by analysis of larval counts of Cheumatopsyche pettiti (Banks), a multiple cohort caddisfly with an aggregated population. The range of mean numbers of C. pettiti was great with low sample numbers. Aggregation is more reliably measured at low sample numbers with the Index of Dispersion and the Mean Crowding Index than with the dispersion parameter k, the calculation of which from the maximum-likelihood equation, is integrally related to sample size. Nonrandom patterns of C. pettiti observed from samples collected in an Indiana, USA, stream riffle, may result from a failure to consider hyporheic distributions, spatial influences (e.g. sampling both favored and nonfavored microhabitats), instar-specific differences, and behavioral features. Variability in secondary production estimates of an aggregated population of Ceraclea ancylus (Vorhies) from a Kentucky, USA, stream indicated similar relationships to sample size.The size of the mean, the degree of aggregation, and the desired precision of the mean estimate will influence the number of samples required to estimate densities of benthic populations. Sample size requirements calculated from data reported in previous studies were high to achieve accepted levels of precision. Habitat stratification may reduce the numbers of samples required. Dicosmoecus gilvipes (Hagen) exhibited nonaggregated patterns and required fewer samples to estimate density in uniform substrate areas of a California, USA, river pool than did aggregated populations in both mixed substrate areas and the entire pool. Ceraclea ancylus required fewer samples for density estimates in stratified (by habitat or substrate type) than unstratified habitats, the fewest samples being necessary when the individual stone was the sampling unit. Judicious choice of study populations may permit larger numbers of samples to be collected and processed with reduced cost, as an alternative to stratification. Larvae of C. ancylus and D. gilvipes could be separated in the field; density underestimation due to a hyporheic population component was eliminated because of surface dwelling behavior or by choice of study sites; and compounded spatial distributions due to co-occurring instar-specific patterns were absent because the populations have a single cohort.Larger numbers of samples may be necessary than are generally taken in benthic studies. Further research is needed to assess variability in secondary production estimates and community diversity analyses. Improved methods for substrate surface area estimation and increased use of experimental approaches and sequential sampling techniques should be considered in future benthic sampling designs. Key words: sampling, benthos, aquatic, macroinvertebrate, Trichoptera, insect, experimental design, autecology, life history, variability

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Louis M. Houston

We derive a general equation for the probability that a measurement falls within a range of n standard deviations from an estimate of the mean. So, we provide a format that is compatible with a confidence interval centered about the mean that is naturally independent of the sample size. The equation is derived by interpolating theoretical results for extreme sample sizes. The intermediate value of the equation is confirmed with a computational test.


1990 ◽  
Vol 47 (1) ◽  
pp. 2-15 ◽  
Author(s):  
Randall M. Peterman

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.


1989 ◽  
Vol 38 (1-2) ◽  
pp. 65-69 ◽  
Author(s):  
Yoko Imaizumi

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.


2021 ◽  
Vol 13 (10) ◽  
pp. 5542
Author(s):  
Dominika Siwiec ◽  
Andrzej Pacana

The main factor that conditions the success of organizations is the development of products oriented toward customer satisfaction. An additional attribute of organizations is the use of sustainable development rules. The use of these rules and the simultaneous desire to create high-quality products encourage organizations to apply different methods to, for example, eliminate waste. This study aimed to develop a method to determine the research sample size required to predict a product’s quality level, taking into account current customers’ expectations. This method was developed by modifying a procedure to determine the research sample size as part of the calculated estimator of the mean value in the general population. Based on the concept of product sustainability development, the goal of the developed method was to determine the number of potential customers (respondents) needed to provide product requirements, which were then processed and used to predict the quality level of the product. This method was applied to simultaneously test a number of hypotheses, determine the test power, and detect statistically significant differences for several relationships of the sample sizes and the test power. This was achieved using universal hypotheses and the popular alternative-punctual (MAP) method. Testing of the proposed method showed that it was able to predict the quality level of products based on current expectations of customers.


2021 ◽  
Vol 43 (5) ◽  
Author(s):  
João Claudio Vilvert ◽  
Sérgio Tonetto de Freitas ◽  
Maria Aparecida Rodrigues Ferreira ◽  
Eleonora Barbosa Santiago da Costa ◽  
Edna Maria Mendes Aroucha

Abstract The objective of this study was to determine the most efficient sample size required to estimate the mean of postharvest quality traits of ‘Palmer’ mangoes harvested in two growing seasons. A total of 50 mangoes were harvested at maturity stage 2, in winter (June 2020) and spring (October 2020), and evaluated for weight, length, ventral and transverse diameter, skin and pulp L*, C* and hº, dry matter, firmness, soluble solids (SS), titratable acidity (TA) and the SS/TA ratio. According to the results, the coefficient of variation (CV) of fruit quality traits ranged from 2.1% to 18.1%. The highest CV in both harvests was observed for the SS/TA ratio, while the lowest was reported for pulp hº. In order to estimate the mean of physicochemical traits of ‘Palmer’ mangoes, 12 fruits are needed in the winter and 14 in the spring, considering an estimation error of 10% and a confidence interval of 95%. TA and the SS/TA ratio required the highest sample size, while L* and hº required the lowest sample size. In conclusion, the variability was different among physicochemical traits and seasons, implying that different sample sizes are required to estimate the mean of different quality traits in different growing seasons.


2016 ◽  
Vol 5 (1) ◽  
pp. 39 ◽  
Author(s):  
Abbas Najim Salman ◽  
Maymona Ameen

<p>This paper is concerned with minimax shrinkage estimator using double stage shrinkage technique for lowering the mean squared error, intended for estimate the shape parameter (a) of Generalized Rayleigh distribution in a region (R) around available prior knowledge (a<sub>0</sub>) about the actual value (a) as initial estimate in case when the scale parameter (l) is known .</p><p>In situation where the experimentations are time consuming or very costly, a double stage procedure can be used to reduce the expected sample size needed to obtain the estimator.</p><p>The proposed estimator is shown to have smaller mean squared error for certain choice of the shrinkage weight factor y(<strong>×</strong>) and suitable region R.</p><p>Expressions for Bias, Mean squared error (MSE), Expected sample size [E (n/a, R)], Expected sample size proportion [E(n/a,R)/n], probability for avoiding the second sample and percentage of overall sample saved  for the proposed estimator are derived.</p><p>Numerical results and conclusions for the expressions mentioned above were displayed when the consider estimator are testimator of level of significanceD.</p><p>Comparisons with the minimax estimator and with the most recent studies were made to shown the effectiveness of the proposed estimator.</p>


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