Number of replicate specimens

Practical considerations usually limit the number of replicate specimens of each kind that can be exposed for each period of test. At least two are recommended for obvious reasons, and if a larger number can be accommodated in the programme more valuable results can be secured—especially when it is desired to establish the reality of small differences in performance. For statistical analysis, five replicates are desirable. Accounts of statistical planning and analysis are given by F. H. Haynie in Reference 2 and in ASTM 016 1984. 

The specimens are produced from slab(s) or beam(s) compacted in accordance with AASHTO PP 3 (1994) or ASTM WK34713 (2014). The number of replicate specimens to be prepared is nine six specimens are tested at different strain levels in order to develop the fatigue line. The extra specimens may also be tested as desired, if the data appear to include an outlier or if a beam failure occurs directly at a clamp. 

If mass losses on a large number of replicate specimens are determined, it is often observed that there is one most frequently occurring value, and as other values deviate from... 

The number of replicate test specimens depends upon the desired reliability of the results of the test. ASTM G 16 gives guidance for establishing the required number of replicate specimens. In general, as recommended in ASTM G 52, triplicate specimens for each exposure period are sufficient for many seawater corrosion tests. 

The structure of the specimen database is dictated by the fact that the specimen carousel in the chromatograph holds up to 16 samples. The set of analytical parameters associated with each specimen position includes the number of replicate injections, the volume of specimen for each injection, the flow rate of the eluting solvent, the duration of the chromatogram, the detector gain, and various timing parameters. A phantom zeroth specimen position is used to define the analytical parameters for injections not specifically programmed into the microprocessor. The operator must manually enter these parameters into the chromatograph s internal microprocessor in order to analyze the specimens in the carousel. 

The quality of data presented can be inferred from the standard deviations or standard error associated with the mean values. In some cases the error can be attributed to either small sample populations or specimen preparation. Where possible, either the number of specimens used or the number of replications of a measurement was reported. The reader... 

Small Specimen Testing—Small specimen testing allows a large number of variables to be evaluated at minimal cost. Small specimens can be designed to determine specific types of corrosion for example, creviced specimens can be exposed to assess localized corrosion [69]. ASTM G16 provides guidance for establishing the number of replicate samples required for the desired reliability of the test results. 

This situation is most likely to occur at the following test conditions at small strains (around 1 % and less) where levels of applied stress are close where the number of replicate tests are small. Common sources of experimental variation are test method reproducibility specimen-to-specimen variation due to fabrication and composition variables, reproducibility of temperature control and variation in loading rate and technique. For certain types of stresses, such as bending and compression, it is possible that variation in stress distribution within the specimens at different levels of applied stress also contribute. 

There is an obvious interest in characterizing the ductile fracture of novel ductile polymeric materials, such as nanocomposites or block copolymers by EWF. However, the amount of such specialty materials produced at laboratory scale may not be large enough and sometimes this can hinder the determination of EWF parameters, since a large number of replicates are usually required for reproducible and reliable results. Therefore, it is the aim of this work to evaluate how much the geometry of DDENT specimens can be reduced without affecting the validity of the results obtained by EWF. 

Direct isolation of sufficient quantities of each metabolite for structural characterization, assay validation and pharmacological or toxicological testing from in vivo studies using biological specimens is, therefore, often impossible, particularly from dmgs with a low therapeutic index. Furthermore, many metabolites have structural modifications which are difficult to replicate by traditional chemical methods. A number of synthetic steps may be required to prepare such metabolites from the API, or, in the worst case, a completely new synthetic route may need to be developed. 

There are a number of indicators of fatigue damage that have attracted interest in the literature. During the life of a component subjected to fatigue, the material can exhibit changes in modulus, permanent offset strain, shape of the hysteresis loops, and temperature rise of the specimen surface. Direct evidence of matrix crack density can be obtained by surface replication, while a more detailed analysis of microstructural damage requires scanning electron microscopy (SEM). 

Table 2. Total numbers of specimens and species of complete and fragmented foraminifera in 7 replicate samples from Kaplan East site the eastern Equatorial Pacific ( 15 °N, 119 °W 4100 m water depth). Data from Nozawa (2005)...

Although the standard deviation gives a measure of the spread of a set of results about the mean value, it does not indicate the shape of the distribution. To illustrate this we need a large number of measurements such as those in Table 2.1. This gives the results of 50 replicate determinations of the nitrate ion concentration in a particular water specimen, given to two significant figures. 

A number of statistical transformations have been proposed to quantify the distributions in pitting variables. Gumbel is given the credit for the original development of extreme value statistics (EVS) for the characterization of pit depth distribution [13]. The EVS procedure is to measure maximum pit depths on several replicate specimens that have pitted, then arrange the pit depth values in order of increasing rank. The Gumbel distribution expressed in Eq 1, where X and a are the location and scale parameters, respectively, can then be used to characterize the dataset and estimate the extreme pit depth that possibly can affect the system from which the data was initially produced. 

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