A precision study should be conducted to provide a better estimate of procedure variability. The precision study may be designed to determine intermediate precision (which includes the components of both ―between run‖ and ―within-run‖ variability) and repeatability (―within-run‖ variability). The intermediate precision studies should allow for changes in the experimental conditions that might be expected, such as different analysts, different preparations of reagents, different days, and different instruments. To perform a precision study, the test is repeated several times. Each run must be completely independent of the others to provide accurate estimates of the various components of variability. In addition, within each run, replicates are made in order to estimate repeatability. See an example of a precision study in Appendix B.
应该进行方法的精密度研究以更好地提供方法变异性评估。可以通过测定算中间精密度（包括“组间between run”变异性和“组内within-run”变异性两个成分）和重复性（“组内within-run”变异性）设计精密度研究。中间精密度研究应允许一些可能的实验条件变化，如不同的分析人员、不同的试剂配置方法、不同的检测时间和不同的检测仪器等。进行精密度研究，实验检测需要进行多次重复。每次运行都必须是彼此完全独立的，以便提供各变异性成分的准确评价。此外，在每个实验组内，必须进行重复测定以便评估其重复性。具体可参见附录 B给出的精密度研究实例。
A confidence interval for the mean may be considered in the interpretation of data. Such intervals are calculated from several data points using the sample mean (x) and sample standard deviation(s) according to the formula:
均值的置信区间也可以用于解释数据资料。置信区间可以通过使用均值和标准偏差根据下面的公式进行计算：
in which ta/2, n? 1 is a statistical number dependent upon the sample size (n), the number of degrees of freedom (n? 1), and the desired confidence level (1 ? a). Its values are obtained from published tables of the Student t-distribution. The confidence interval provides an estimate of the range within which the ―true‖ population mean (μ) falls, and it also evaluates the reliability of the sample mean as an estimate of the true mean. If the same experimental set-up were to be replicated over and over and a 95% (for example) confidence interval for the true mean is calculated each time, then 95% of such intervals would be expected to contain the true mean, μ. One cannot say with certainty whether or not the confidence interval derived from a specific set of data actually collected contains μ. However, assuming the data represent mutually independent measurements randomly generated from a normally distributed population, the procedure used to construct the confidence interval guarantees that 95% of such confidence intervals containμ. Note that it is important to define the population appropriately so that all relevant sources of variation are captured. [NOTE ON TERMINOLOGY—In the documents of the International Organization for Standardization (ISO), different terminology is used for some of the concepts described here. The term s/
, which is commonly called the standard
is called the
error of the mean, is called the standard uncertainty in ISO documents. The term ta/2, n? 1 S/
expanded uncertainty, and ta/2, n? 1 is called the coverage factor, by ISO. If the standard deviation is found by
combining estimates of variability from multiple sources, it is called the combined standard uncertainty. Some of these sources could have nonstatistical estimates of uncertainty, called Type B uncertainties, such as uncertainty in calibration of a balance.] 在该公式中，t
/2,n–1是一个统计数据，其大小取决于样本量大小（n），自由度（n-1）；及期望的置信水平(1 –
)。其值可以通过发表的学生t-分布表查到。置信区间评估了一个总体真实均值（μ）存在的区间，它同时也是评价样本均值代表真值可靠性的指标。如果同样的实验设计重复多次，并且假定每次都计算真实均值的95%置信区间，那么这些置信区间中有95%将会期望包含真值μ。但人们不能一定说从一个实际采集的数据衍生出来的置信区间是包含或不包含真值μ。但是，假设所用数据代表了正态分布总体中的相互独立的随机测量，那么用于计算置信区间的方法可以担保有95%的置信区间包含真值μ。应该注意的是，为了能采集到所用的相关变异因素，正确定义总体是非常重要的。[注意术语的使用，在ISO的文件当中对于上述一些概念使用了不同的术语。s/
通常被称为均值的标准误，而在ISO文件当中称为标准不确定度。在ISO文件中，t
/2,n–1S/
被称为扩展不
确定度，t
/2,n–1被称为包含因子。如果标准偏差合并了对于多种来源变异性的估计，那么其被称为合成标准不
确定度。这些变异性来源中的一些可能是对于不确定度的非统计性评估，他们被称为B类不确定度，比如天平校准的不确定度。]
OUTLYING RESULTS
异常结果
Occasionally, observed analytical results are very different from those expected. Aberrant, anomalous, contaminated, discordant, spurious, suspicious or wild observations; and flyers, rogues, and mavericks are properly called outlying results. Like all laboratory results, these outliers must be documented, interpreted, and managed. Such results may be accurate measurements of the entity being measured, but are very different from what is expected. Alternatively, due to an error in the analytical system, the results may not be typical, even though the entity being measured is typical. When an outlying result is obtained, systematic laboratory and, in certain cases, process investigations of the result are conducted to determine if an assignable cause for the result can be established. Factors to be considered when investigating an outlying result include—but are not limited to—human error, instrumentation error, calculation error, and product or component deficiency. If an assignable cause that is not related to a product or component deficiency can be identified, then retesting may be performed on the same sample, if possible, or on a new sample. The precision and accuracy of the procedure, the USP Reference Standard, process trends, and the specification limits should all be examined. Data may be invalidated, based on this documented investigation, and eliminated from subsequent calculations.
有时，我们观察到的结果和我们预期的有很大差距。异常的、反常的、被污染的、不一致的、虚假的、可疑的或者是不受控制的观察值；还有离群值，异常值和异端值都应该叫做异常结果。像所有实验结果一样，这些异常值也必须进行记录、解释说明和处理。这些结果有可能是被测物的正确测量值，只是和我们的预期有很大
的差距。相应地，即使被测总体符合典型特征，这些结果也有可能由于一个分析体系中的错误而变成非典型的。当得到一个异常值的时候，就要对该值进行系统的实验室调查，在某些情况下还要进行实验过程调查，以确定产生异常值是否有一个明确的原因（assignable cause）。产生异常值的明确原因通常有但不限于以下几点——人为错误，仪器错误，计算错误，产品或者组分缺陷。如果产生异常值的明确原因可以确定与产品或者组分缺陷有关，那么如果可能就对同一样本进行重复实验，或者用新的样本进行重复实验。对于方法的精密性和准确性，USP参考品，过程趋势和规定标准限值等都要进行审核。基于这些经过证明的调查，数据有可能被发现是无效的，这时需将它从后续的计算中删除。
If no documentable, assignable cause for the outlying laboratory result is found, the result may be tested, as part of the overall investigation, to determine whether it is an outlier.
如果没有发现实验室结果异常存在可证明的明确原因，那么它要作为整体研究的一部分进行检验，以确定它是否是个异常值。
However, careful consideration is warranted when using these tests. Two types of errors may occur with outlier tests: (a) labeling observations as outliers when they really are not; and (b) failing to identify outliers when they truly exist. Any judgment about the acceptability of data in which outliers are observed requires careful interpretation.
但是，当进行这些检验的时候一定要小心谨慎。在进行异常值检验的时候会犯两类错误。第一类是将不是异常值的值当做异常值；第二类是把异常值当做正常值。对于观测到异常值的数据可接受性，所做出的任何一种判断都要进行详细的解释。
―Outlier labeling‖ is informal recognition of suspicious laboratory values that should be further investigated with more formal methods. The selection of the correct outlier identification technique often depends on the initial recognition of the number and location of the values. Outlier labeling is most often done visually with graphical techniques. ―Outlier identification‖ is the use of statistical significance tests to confirm that the values are inconsistent with the known or assumed statistical model.
“异常值的标识(outlier labeling)”是对可疑实验数据的非正式识别，要用更正规的方法进一步调查。异常值正确识别方法的选择通常依赖于对数值的数目和位置的初步识别。标志异常值通常用绘图方法进行目视标识。“异常值识别（Outlier identification）”是使用统计学显著性方法来确定数值在已知的或假定的统计模型中是异常的。 When used appropriately, outlier tests are valuable tools for pharmaceutical laboratories. Several tests exist for detecting outliers. Examples illustrating three of these procedures, the Extreme Studentized Deviate (ESD) Test, Dixon's Test, and Hampel's Rule, are presented in Appendix C.
如果使用得当，异常值检验对于药品领域的实验室来说非常有用。有几个方法可以用于异常值检验。在附录C中有3个例子：极端学生化偏离检验（ESD检验）、狄克逊检验（Dixon检验）和Hampel规则
Choosing the appropriate outlier test will depend on the sample size and distributional assumptions. Many of these tests (e.g., the ESD Test) require the assumption that the data generated by the laboratory on the test results can be
thought of as a random sample from a population that is normally distributed, possibly after transformation. If a transformation is made to the data, the outlier test is applied to the transformed data. Common transformations include taking the logarithm or square root of the data. Other approaches to handling single and multiple outliers are available and can also be used. These include tests that use robust measures of central tendency and spread, such as the median and median absolute deviation and exploratory data analysis (EDA) methods. ―Outlier accommodation‖ is the use of robust techniques, such as tests based on the order or rank of each data value in the data set instead of the actual data value, to produce results that are not adversely influenced by the presence of outliers. The use of such methods reduces the risks associated with both types of error in the identification of outliers.
选择合适的异常值检验方法取决于样本量大小和分布假设。许多检验方法（如ESD检验）要求假设实验室结果数据是来自一个正态分布总体或者转换成正态分布总体的随机样本。如果对数据进行了转换，则异常值检验方法适用于转换后的数据。常见的转换方法包括取对数转换或平方根转换。其他处理单个或者多个异常值的方法也可以使用。这些方法包括集中趋势和离散趋势的稳健分析方法，比如中位数、中位数绝对偏差（median absolute deviation）和探索性数据分析（EDA）方法。“异常值的调适（Outlier accommodation）”是利用稳健方法使得出的结果不会因异常值存在而产生不利影响，比如可以使用每个数据值在整个数据集中的序或秩来替代原数据进行分析。使用这些方法会降低了异常值识别过程中出现上述两类错误的风险。
―Outlier rejection‖ is the actual removal of the identified outlier from the data set. However, an outlier test cannot be the sole means for removing an outlying result from the laboratory data. An outlier test may be useful as part of the evaluation of the significance of that result, along with other data. Outlier tests have no applicability in cases where the variability in the product is what is being assessed, such as content uniformity, dissolution, or release-rate determination. In these applications, a value determined to be an outlier may in fact be an accurate result of a nonuniform product. All data, especially outliers, should be kept for future review. Unusual data, when seen in the context of other historical data, are often not unusual after all but reflect the influences of additional sources of variation.
“异常值的剔除（Outlier rejection）”是将识别出的异常值从数据集中剔除。但是，异常值检验不是将异常值从实验数据中剔除的唯一方法。异常值检验连同其他数据一起，作为对结果显著性评估的一部分是很有用的。当实验目的就是评价产品的变异性时，如在含量均匀度、溶出性或释放速率实验中，异常值检验是无法使用的。在这种情况下，被确定为异常值的一个结果实际上就是确定产品不均一的一个准确结果。所有数据，尤其是异常值，要保留下来供今后进一步的评估。当放在其它历史数据中一起审视时，一些异常的数据很可能就不是异常的了，而是反映出其他变异性来源的影响。
In summary, the rejection or retention of an apparent outlier can be a serious source of bias. The nature of the testing as well as scientific understanding of the manufacturing process and analytical procedure have to be considered to determine the source of the apparent outlier. An outlier test can never take the place of a thorough laboratory investigation. Rather, it is performed only when the investigation is inconclusive and no deviations in the manufacture