Error alpha

Type I error (alpha error) An incorrect decision resulting from rejecting the null hypothesis when the null hypothesis is true. A false positive decision. 

Sample sizes for the sub-studies have been calculated based on a two-sided alpha error of 0.1 and a power of 0.8, but have also been tempered by considerations of logistical feasibility. Preliminary piloting of the instrument confirms these estimates. In particular, the simulation exercise has been considered relatively selectively for a smaller number of subjects and target groups, largely because of anticipated logistical constraints. 

There are several ways to reduce both type I and type II errors available to researchers. First, one can select a more powerful statistical method that reduces the error term by blocking, for example. This is usually a major goal for researchers and a primary reason they plan the experimental phase of a study in great detail. Second, as mentioned earlier, a researcher can increase the sample size. An increase in the sample size tends to reduce type II error, when holding type I error constant that is, if the alpha error is set at 0.05, increasing the sample size generally will reduce the rate of beta error. 

The relative value placed upon avoiding Type I and Type II errors can be appreciated by examining the ratio of beta level to alpha level at sample sizes generally seen in the lead literature. Table 1, taken from a recent communication by Rosenthal and Rubin (1985), is informative. It shows that for a sample size between 80 and 200, at an r of 0.10, the ratio of accepted beta to alpha error can be as high as 17 to 1. 

The advantage of unrestricted calculations is that they can be performed very efficiently. The alpha and beta orbitals should be slightly different, an effect called spin polarization. The disadvantage is that the wave function is no longer an eigenfunction of the total spin <(5 >. Thus, some error may be introduced into the calculation. This error is called spin contamination and it can be considered as having too much spin polarization. 

In addition to these formal studies of human error in the CPI, almost all the major accident investigations in recent years, for example, Texas City, Piper Alpha, Phillips 66, Feyzin, Mexico City, have shown human error as a significant causal factors in design, operations, maintenance or the management of the process. Figures 4.4-1 and 4.4-2 show the effects of human error on nuclear plant operation. 

The case study has documented the investigation and root cause analysis process applied to the hydrocarbon explosion that initiated the Piper Alpha incident. The case study serves to illustrate the use of the STEP technique, which provides a clear graphical representation of the agents and events involved in the incident process. The case study also demonstrates the identification of the critical events in the sequence which significantly influenced the outcome of the incident. Finally the root causes of these critical events were determined. This allows the analyst to evaluate why they occurred and indicated areas to be addressed in developing effechve error reduchon strategies. 

If the sequence of a protein has more than 90% identity to a protein with known experimental 3D-stmcture, then it is an optimal case to build a homologous structural model based on that structural template. The margins of error for the model and for the experimental method are in similar ranges. The different amino acids have to be mutated virtually. The conformations of the new side chains can be derived either from residues of structurally characterized amino acids in a similar spatial environment or from side chain rotamer libraries for each amino acid type which are stored for different structural environments like beta-strands or alpha-helices. 

The two error types mentioned in the title are also designated with the Roman numerals I and II the associated error probabilities are termed alpha (a) and beta ( 8). 

Figure 2.9. The confidence interval for an individual result CI( 3 ) and that of the regression line s CLj A are compared (schematic, left). The information can be combined as per Eq. (2.25), which yields curves B (and S, not shown). In the right panel curves A and B are depicted relative to the linear regression line. If e > 0 or d > 0, the probability of the point belonging to the population of the calibration measurements is smaller than alpha cf. Section 1.5.5. The distance e is the difference between a measurement y (error bars indicate 95% CL) and the appropriate tolerance limit B this is easy to calculate because the error is calculated using the calibration data set. The distance d is used for the same purpose, but the calculation is more difficult because both a CL(regression line) A and an estimate for the CL( y) have to be provided.

Zero sampling error Eq. (1.6) reduces to Vreprod = V repeat-Independent individual samples/measurements in the sense of Fig. 1.5. A result that is nearer to the SL(/ and an accepted risk of 5% (alpha/2 = 0.05 for the single-sided test use the alpha = 0.1 column in the t-table). 

In Fig. 3 all measured data of fj = cjf/cj and f are presented as function of the attachment rate. The absolute errors of fj and fp are in the order of 0.02 and 0.01, respectively. These errors were calculated from the errors due to the alpha counting and due to the mathematical procedure of the filter activity evaluation. 

It is desirable that the number of false positives be small (i.e., there should be a low type I error rate or alpha level). 

Type I Error (false positives) Concluding that there is an effect when there really is not an effect. Its probability is the alpha level... 

Duncan s assures a set alpha level or type I error rate for all tests when means are separated by no more than ordered step increases. Preserving this alpha level means that the test is less sensitive than some others, such as the Student-Newman-Keuls. The test is inherently conservative and not resistant or robust. 

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