扰其准确性、线性和精密度后，一个已确认过的方法才可用于检测一个新的处方（如新产品、新剂型或中间产物）的检测。我们不能假定，一个经过确认的方法就一定能准确检测在不同处方中的活性成分。[注意术语的使用，“准确性（accuracy）”在<1225>和ICH Q2中的定义是仅仅相当于无偏倚性，在国际计量学词汇(VIM)和ISO文件中，accuracy有不同的意思。ISO文件中的accuracy包含不偏倚性（使用真实性trueness的术语）和精密度。本章所采用的概念是根据<1225>的定义，即仅相当于真实性trueness。]
MEASUREMENT PRINCIPLES AND VARIATION
测量原则和变异性
All measurements are, at best, estimates of the actual (―true‖ or ―accepted‖) value for they contain random
variability (also referred to as random error) and may also contain systematic variation (bias). Thus, the measured value differs from the actual value because of variability inherent in the measurement. If an array of measurements consists of individual results that are representative of the whole, statistical methods can be used to estimate informative properties of the entirety, and statistical tests are available to investigate whether it is likely that these properties comply with given requirements. The resulting statistical analyses should address the variability associated with the measurement process as well as that of the entity being measured. Statistical measures used to assess the direction and magnitude of these errors include the mean, standard deviation, and expressions derived therefrom, such as the percent coefficient of variation (%CV; also called the percent relative standard deviation, %RSD). The estimated variability can be used to calculate confidence intervals for the mean, or measures of variability, and tolerance intervals capturing a specified proportion of the individual measurements.
所有的测量都只能最多说用于估计实际值（“真值”或“认可值”），因为它们都包含随机变异（也叫随机误差）和可能的系统变异（偏倚）。所以，被测值与实际值因这些测量固有的变异而存在差异。如果一系列包含单独检测结果的测量是整个总体的代表，那么就可以使用统计方法对总体的特征信息进行估计，并且可以使用统计检验的方法判断这些特性是否符合规定要求。所得到的统计分析结果应该显示出所有测量过程和测量总体相关的变异性。用于评估这些误差的方向和程度的统计量包括均值、标准差及由此衍生的一些表述，例如百分变异系数（%CV，也叫百分相对标准偏差，%RSD）。所评估的变异性可以用于计算均值的置信区间，或测量变异性，以及计算用于捕捉特定比例单次测量的容忍区间（tolerance intervals）。
The use of statistical measures must be tempered with good judgment, especially with regard to representative sampling. Data should be consistent with the statistical assumptions used for the analysis. If one or more of these assumptions appear to be violated, alternative methods may be required in the evaluation of the data. In particular, most of the statistical measures and tests cited in this chapter rely on the assumptions that the distribution of the entire population is represented by a normal distribution and that the analyzed sample is a representative subset of this population. The normal (or Gaussian) distribution is bell-shaped and symmetric about its center and has certain characteristics that are required for these tests to be valid. The data may not always be expected to be normally distributed and may require a transformation to better fit a normal distribution. For example, there exist variables that
have distributions with longer right tails than left. Such distributions can often be made approximately normal through a log transformation. An alternative approach would be to use ―distribution-free‖ or ―nonparametric‖ statistical procedures that do not require that the shape of the population be that of a normal distribution. When the objective is to construct a confidence interval for the mean or for the difference between two means, for example, then the normality assumption is not as important because of the central limit theorem. However, one must verify normality of data to construct valid confidence intervals for standard deviations and ratios of standard deviations, perform some outlier tests, and construct valid statistical tolerance limits. In the latter case, normality is a critical assumption. Simple graphical methods, such as dot plots, histograms, and normal probability plots, are useful aids for investigating this assumption. 使用统计量必须有良好的判断加以调节，特别是要考虑到抽样的代表性。数据必须与分析用到的统计假设相一致。如果有一个或多个假设出现不相符，则需要采用替代的方法进行数据评价。特别应指出的是，本章所引用的统计量和检验都是基于假设总体符合正态分布，并且假设所分析的样本是能代表总体的一个亚体（Subset）。正态分布（也叫高斯分布）是一个钟形且呈中心对称形状的分布，并且有一些用于检验的特征需要被验证。数据并非总是符合正态分布的，这时需要进行适当转换以便其更好地符合正态分布。例如，存在着一些变量具有长右尾分布。这样的分布经常通过对数转换将其变为符合近似正态分布。也可使用“不依赖于分布”或“非参数”的替代统计方法，该类方法不要求数据符合正态分布。当目标是计算均值或两均值差的置信区间时，那么，其正态性假设就因中心极限定理（central limit theorem）而不再那么重要。但是，人们必须首先验证数据的正态性，才能计算出正确有效的标准差的置信区间和标准偏差比、进行异常值检测并且计算出正确有效的统计容忍限等。对于后者，正态性是至关重要的假设。一些简单的作图法（如散点图、柱状图和正态概率图）对于分析正态性假设非常有用。
A single analytical measurement may be useful in quality assessment if the sample is from a whole that has been prepared using a well-validated, documented process and if the analytical errors are well known. The obtained analytical result may be qualified by including an estimate of the associated errors. There may be instances when one might consider the use of averaging because the variability associated with an average value is always reduced as compared to the variability in the individual measurements. The choice of whether to use individual measurements or averages will depend upon the use of the measure and its variability. For example, when multiple measurements are obtained on the same sample aliquot, such as from multiple injections of the sample in an HPLC method, it is generally advisable to average the resulting data for the reason discussed above.
除非样本来自于一个使用经充分确认过且经过证明的方法制备的总体，并且其分析误差已知，那么这样一个单次分析测量在质量评价中才会是有用的。在引入了相关误差的评估后该分析结果才能满足要求。有些情况可以考虑使用均值，因为与单一的各测量值比较，均值的变异总是很小。究竟使用单个测量值还是使用其均值的选择，主要依赖于所用的测量和其变异性。例如，当可以从样本的组分中获得多个测量值时，如在使用液相方法对同一样本进行多次检测时，根据前述原因一般建议使用结果的均值。
Variability is associated with the dispersion of observations around the center of a distribution. The most
commonly used statistic to measure the center is the sample mean (x):
变异性与围绕分布中心的观测离散性相关。最常见的用于计算中心位置的统计量就是样本均值：
Analytical procedure variability can be estimated in various ways. The most common and useful assessment of a procedure's variability is the determination of the standard deviation based on repeated independent1 measurements of a sample. The sample standard deviation, s, is calculated by the formula:
方法的变异性可以有各种方式进行评估。对于方法变异性最常见和有用的评估指标是针对样本重复性独立性1测量值的标准差。样本标准差的计算公式如下：
in which xi is the individual measurement in a set of n measurements; and x is the mean of all the measurements. The percent relative standard deviation (%RSD) is then calculated as:
在公式中，xi是一系列测量中某一个测量值，x为所有测量值的均值。百分相对标准偏差（%RSD）的计算如下：
and expressed as a percentage. If the data requires log transformation to achieve normality (e.g., for biological assays), then alternative methods are available2.
百分相对标准偏差用百分数表示。如果数据需要进行对数转换才能达到正态性（如一些生物检定实验），那么应使用另一种替代计算方法2。
1
Multiple measurements (or, equivalently, the experimental errors associated with the multiple measurements) are independent from one another when they can be assumed to represent a random sample from the population. In such a sample, the magnitude of one measurement is not influenced by, nor does it influence the magnitude of, any other measurement. Lack of independence implies the measurements are correlated over time or space. Consider the example of a 96-well microtiter plate. Suppose that whenever the
unknown causes that produce experimental error lead to a low result (negative error) when a sample is placed in the first column and these same causes would also lead to a low result for a sample placed in the second column, then the two resulting measurements would not be statistically independent. One way to avoid such possibilities would be to randomize the placement of the samples on the plate. 当可以假定他们是来自于同一整体的随机代表样本，多次测量是彼此独立的，同样的与多次测量相关的实验误差也是彼此独立的。在这样一个样本中，单次测量的量值不会被其他的测量所干扰，也不会干扰其他测量的量值。缺乏独立性意味着测量值与时间或空间相关。想象下96孔板的例子。假设当样品置于第一孔上时未知的实验误差因素在任何时候都导致一个偏低的结果（阴性结果），这些相同的因素也对第二孔上的样本导致了偏低结果，那么这两个测量结果就不能满足独立性的要求。避免这种可能性的一种方法是在板上随机放置样本。
2
When data have been log (base e) transformed to achieve normality, the %RSD is:
This can be reasonably approximated by: where s is the standard deviation of the log (base e) transformed data. 当数据进行了对数转换以获得正态性后，%RSD计算公式为 也可以合理的简化为 其中s是自然对数转换后数据的标准偏差。
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