Testing biases

Are control charts recorded for each determinand to test bias and reproducibility yes/no/not known WFD, SOE Cat. 3... 

Cleary, T. A. (1968). Test bias Prediction of grades of Negro and White students in integrated colleges. Journal of Educational Measurement, 5, 115-124. 

Bias—Test bias can result if the kauri-butanol solution is not carefully standardized and adjusted (see 7.2 and 7.3). The test method has no definitive bias statement because the value of the test result is defined only in terms of the test method. 

A simple method of improvmg the effieieney of test partiele insertion [106. 107. 108 and 109] involves dividing die simulation box into small eubie regions, and identifying those whieh would make a negligible eontribution to the Widom fonnula, due to overlap with one or more atoms. These eubes are exeluded from the sampling, and a eorreetion applied afterwards for the eonsequent bias. 

Sometimes just one determination is available on each of several known materials similar in composition. A single determination by each of two procedures (or two analysts) on a series of material may be used to test for a relative bias between the two methods, as in Example 2.4. Of course, the average difference does not throw any light on which procedure has the larger constant error. It only supplies a test as to whether the two procedures are in disagreement. 

Analytical chemists make a distinction between error and uncertainty Error is the difference between a single measurement or result and its true value. In other words, error is a measure of bias. As discussed earlier, error can be divided into determinate and indeterminate sources. Although we can correct for determinate error, the indeterminate portion of the error remains. Statistical significance testing, which is discussed later in this chapter, provides a way to determine whether a bias resulting from determinate error might be present. 

A newly proposed method is to be tested for its singleoperator characteristics. To be competitive with the standard method, the new method must have a relative standard deviation of less than 10%, with a bias of less than 10%. To test the method, an analyst performs ten replicate analyses on a standard sample known to contain 1.30 ppm of the analyte. The results for the ten trials are... 

Analytical and Test Methods. Many of the procedures for technical analyses of magnesium hydroxide are readily available from the principal producers. These procedures should be carefully reviewed. Site-specific variations in procedure steps and mechanics, especially for chemical activity, can bias results and inadvertantiy disqualify an otherwise acceptable product. 

With the Monte Carlo method, the sample is taken to be a cubic lattice consisting of 70 x 70 x 70 sites with intersite distance of 0.6 nm. By applying a periodic boundary condition, an effective sample size up to 8000 sites (equivalent to 4.8-p.m long) can be generated in the field direction (37,39). Carrier transport is simulated by a random walk in the test system under the action of a bias field. The simulation results successfully explain many of the experimental findings, notably the field and temperature dependence of hole mobilities (37,39). 

Other polyamides having higher moduli and T than nylon-6 and nylon-6,6 have been evaluated in an effort to reduce wrinkle resistance and eliminate flat-spotting of bias and bias-belted tires (Table 3). Nylons have also been tested extensively over the years for apparel and carpets (Table 4). 

Statistical Control. Statistical quahty control (SQC) is the apphcation of statistical techniques to analytical data. Statistical process control (SPC) is the real-time apphcation of statistics to process or equipment performance. Apphed to QC lab instmmentation or methods, SPC can demonstrate the stabihty and precision of the measurement technique. The SQC of lot data can be used to show the stabihty of the production process. Without such evidence of statistical control, the quahty of the lab data is unknown and can result in production challenging adverse test results. Also, without control, measurement bias cannot be determined and the results derived from different labs cannot be compared (27). 

Preferably the transferring lab provides a sample which has already been analyzed, with the certainty of the results being known (41). This can be either a reference sample or a sample spiked to simulate the analyte. An alternative approach is to compare the test results with those made using a technique of known accuracy. Measurements of the sample are made at the extremes of the method as well as the midpoint. The cause of any observed bias, the statistical difference between the known sample value and the measured value, should be determined and eliminated (42). When properly transferred, the method allows for statistical comparison of the results between the labs to confirm the success of the transfer. 

The accuracy of microhardness testing has been questioned a wide range of values appears in the Hterature for plated deposits, especially in hardness extremes. ASTM B8.10 is involved in intedaboratory testing to define the precision and bias of the Specification B576 Microhardness of Electroplated Coatings (55,56). 

J. Homer, "Microhardness Testing of Plating Coatings Defining Precision and Bias," Inf/Tech Conf. Proc., AESF SUR/FIN, Atianta, Ga., 1992. 

Electrical Properties. Electrical properties are important for the corrosion protection of chip-on-board (COB) encapsulated devices. Accelerated temperature, humidity, and bias (THB) are usually used to test the embedding materials. Conventional accelerating testing is done at 85°C, 85% relative humidity, and d-c bias voltage. Triple-track test devices with tantalum nitride (Ta2N), titanium—palladium—gold (Ti—Pd—Au) metallizations with 76... 

Fig. 6. Temperature—humidity—bias leakage testing of encapsulants at 85°C, 85% rh, and 180 V bias.

Mathematical Consistency Requirements. Theoretical equations provide a method by which a data set s internal consistency can be tested or missing data can be derived from known values of related properties. The abiUty of data to fit a proven model may also provide insight into whether that data behaves correctiy and follows expected trends. For example, poor fit of vapor pressure versus temperature data to a generally accepted correlating equation could indicate systematic data error or bias. A simple sermlogarithmic form, (eg, the Antoine equation, eq. 8), has been shown to apply to most organic Hquids, so substantial deviation from this model might indicate a problem. Many other simple thermodynamics relations can provide useful data tests (1—5,18,21). 

Quality control elements required by the instrumental analyzer method include analyzer calibration error ( 2 percent of instrument span allowed) verifying the absence of bias introduced by the sampling system (less than 5 percent of span for zero and upscale cah-bration gases) and verification of zero and calibration drift over the test period (less than 3 percent of span of the period of each rim). 

Rectification accounts for systematic measurement error. During rectification, measurements that are systematically in error are identified and discarded. Rectification can be done either cyclically or simultaneously with reconciliation, and either intuitively or algorithmically. Simple methods such as data validation and complicated methods using various statistical tests can be used to identify the presence of large systematic (gross) errors in the measurements. Coupled with successive elimination and addition, the measurements with the errors can be identified and discarded. No method is completely reliable. Plant-performance analysts must recognize that rectification is approximate, at best. Frequently, systematic errors go unnoticed, and some bias is likely in the adjusted measurements. 

Should the additional component compositions be required to fully understand the unit operation, the laboratory may have to develop new analysis procedures. These must be tested and practiced to establish reliabihty and minimize bias. Analysts must sribmit known samples to verify the accuracv. 

If the random errors are higher than can be tolerated to meet the goals of the test, the errors can be compensated for with rephcate measurements and a commensurate increase in the laboratory resources. Measurement bias can be identified through submission and analysis of known samples. Establishing and justifying the precision and accuracy reqrtired by the laboratory is a necessary part of estabhshing confidence. 

Representativeness can be examined from two aspects statistical and deterministic. Any statistical test of representativeness is lacking becau.se many histories are needed for statistical significance. In the absence of this, PSAs use statistical methods to synthesize data to represent the equipment, operation, and maintenance. How well this represents the plant being modeled is not known. Deterministic representativeness can be answered by full-scale tests on like equipment. Such is the responsibility of the NSSS vendor, but for economic reasons, recourse to simplillcd and scaled models is often necessary. System success criteria for a PSA may be taken from the FSAR which may have a conservative bias for licensing. Realism is more expensive than conservatism. 

It is important to keep in mind that statistically based studies by themselves can never prove the e.xistence of a cause and effect relationship. However, such obseix ations may be used to generate or to test a hypothesis. Many possibilities exist for introducing bias in this type of investigation, and statistical correlations may be fortuitous. 

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