Systematic errors data reduction

Microcomposite tests including fiber pull-out tests are aimed at generating useful information regarding the interface quality in absolute terms, or at least in comparative terms between different composite systems. In this regard, theoretical models should provide a systematic means for data reduction to determine the relevant properties with reasonable accuracy from the experimental results. The data reduction scheme must not rely on the trial and error method. Although there are several methods of micromechanical analysis available, little attempt in the past has been put into providing such a means in a unified format. A systematic procedure is presented here to generate the fiber pull-out parameters and ultimately the relevant fiber-matrix interface properties. 

However, wide systematic investigations in molten salts require unified experimental techniques which provide a reduction in the data-spreads which arise from the systematic errors characteristic of different methods. This makes the obtained regularities better substantiated. 

We also discuss the analysis of the accuracy of experimental data. In the case that we can directly measure some desired quantity, we need to estimate the accuracy of the measurement. If data reduction must be carried out, we must study the propagation of errors in measurements through the data reduction process. The two principal types of experimental errors, random errors and systematic errors, are discussed separately. Random errors are subject to statistical analysis, and we discuss this analysis. 

It is worth noting that the first systematic EPR analysis on Msba was published in 2005 [44] and the data were compared to the X-ray structures of MsbA in the open and closed states published from 2001 to 2005, which were retracted in 2007 due to data misinterpretation caused by an error in in-house data reduction software. The comparison of the EPR data with the corrected crystal structures reviewed here [26] resolved the earlier incongruencies brought about by wrong helix assignment. 

The three different data sets produce extreme differences In the temperature factors (Table VIII). This difference Is not characteristic of just the SRRC program, as a similar range for cellulose Is In the literature. The temperature factor (B) Is Important because It Indicates systematic error In the Lp correction or other aspect of data gathering and reduction, for at least two of the data sets. Negative temperature factors, as found In the WS data, typically Indicate either that additional atoms, such as water molecules, are needed In the structure, or, when that Is known to be Incorrect, that there Is a flaw In some overall aspect of Intensity measurement, such as background correction. 

There is some uncertainty in all data, and model building must take this error into account. The first step in error management is error detection, error reduction, and error quantification. There are three types of error systematic error, random error, and blunders. Improved experimental protocol can reduce all these, but designing progressively better experiments eventually leads to diminishing returns so that at some point it is necessary to use some kind of error analysis to manage the uncertainty in the variable being quantified. 

Although the use of notations such as 5, e, y, and so on is useful to make sometimes minute differences more visible, in addition to making data better comparable between laboratories, owing to the reduction of systematic errors, agreement between scientists or laboratories is often not achieved. This can cause confusion and misinterpretation of published data if htde care is taken about the selection and description of the common reference used for calculating the isotope notation. 

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