Population outliers

Equation (4) provides a value of AICO = 0.00852. As we see, the populations of classes 3 and 8 bear redundancy in information, as their JCOs are quite low here. In contrast, classes 6 and 7 are clearly outliers. Their JCOs are too high in compar-... 

On occasion, a data set appears to be skewed by the presence of one or more data points that are not consistent with the remaining data points. Such values are called outliers. The most commonly used significance test for identifying outliers is Dixon s Q-test. The null hypothesis is that the apparent outlier is taken from the same population as the remaining data. The alternative hypothesis is that the outlier comes from a different population, and, therefore, should be excluded from consideration. 

This experiment uses the change in the mass of a U.S. penny to create data sets with outliers. Students are given a sample of ten pennies, nine of which are from one population. The Q-test is used to verify that the outlier can be rejected. Glass data from each of the two populations of pennies are pooled and compared with results predicted for a normal distribution. 

These workers concluded that the chemical differences among populations from the various sites were too small to support recognition of two species, pointing to the lack of correlation between morphologieal features and alkaloid ehemistry. They noted as well that the outlier chemistry could be the result of developmental. 

Toxicology has long recognized that no population, animal or human, is completely uniform in its response to any particular toxicant. Rather, a population is composed of a (presumably normal) distribution of individuals some resistant to intoxication (hyporesponders), the bulk that respond close to a central value (such as an LD50), and some that are very sensitive to intoxication (hyperresponders). This population distribution can, in fact, result in additional statistical techniques. The sensitivity of techniques such as ANOVA is reduced markedly by the occurrence of outliers (extreme high or low values, including hyper- and hyporesponders), which,... 

A normally distributed population is assumed, and the results are sensitive to outliers. 

For the present data, after replacing outlier values with 4 (ppm, equal to the value of 97% of Au population in data) the relationship between estimated data and reported ones is about 0.6, which make sense, though the amount of data is not enough at all. 

Methods for robust statistics have been developed that deliver good results (i.e. estimation of the population mean) even with a relatively large number of outliers or with a skewed distributiom For more detailed descriptions of these methods please refer to the relevant textbooks. 

The extreme data point X is not an outlier and comes from a normally distributed population with sample mean X and standard deviation s... 

Median Median is a value that can be used to describe the fluorescence intensity of a population of cells. If the cells were lined up in order of increasing intensity, the median value would simply be the channel number or intensity of the cell that is at the midpoint in the sequence. The median channel, because it is unaffected by off-scale events and outliers, is considered by many to be the best way to describe the intensity of a population. See Mean and Mode. 

What is unique about the use of the Grubbs tests is that, before the tests are applied, data are sorted into ascending order. The test values for G G2, and G3 are compared with values obtained from tables (see Table 2.4), as has been common with all the tests discussed previously. If the test values are greater than the tabulated values, we reject the null hypothesis that they are from the same population and reject the suspected values as outliers. Again, the level of confidence that is used in outlier rejection is usually at the 95 and 99% limits. 

We know from Figure 2.5 that the true population mean for erythromycin content is very close to the nominal figure of 250 mg. In both samples an odd outlying high value (marked ) has crept in. Because the first sample is small, the outlier has displaced the sample mean considerably above the true population mean. There is also an odd value in the second sample, but it is now only one observation among 12, and so the sample mean remains much closer to the true value. 

Within a population of similar demographic and health characteristics, a 4 to 6-fold range in the metabolic clearance a CYP3A-drug substrate is usual, with common individual outliers exhibiting high or low activity (Lamba 2002). 

If the number of samples with characteristics presenting a normal distribution is not significant, then we can have an error structure. This situation can also be due to outliers, i.e. samples that are atypical of the population or that might have been incorrectly labeled or grouped. 

---