Vad är sampling error
The objective of this column is to provide easy-to-understand examples of sampling errors.
Sampling Errors in Statistics: Definition, Types, and Calculation
WHAT is a sampling error? WHAT is the result of sampling errors? WHAT can we do about sampling errors? These are welcome topics for a series of sampling columns! The point of departure will be in the Theory of Sampling and in the near infrared spectroscopy analysis and pharma application sectors, but the focus will be developed to be more general , so that readers can carry-over to other scientific and application areas of interest.
It is very instructive to start with how the topic of sampling errors is seen from the point of view of where everything ends up: analysis. What is the point of view from analytical chemistry? At the undergraduate level, students are taught that there are seven basic steps involved in an analytical chemical analysis. These are i method selection, ii sample acquisition, iii sample preparation, iv sample analysis, v calculation and vi interpretation of the results… and vii preparation of a professional report.
The second step in this chemical analysis pathway is known as sampling. One notes that there is no help here as to how to acquire a representative sample and a representative analytical aliquot.
Sampling Errors in Statistics: Definition, Types, and Calculation
However, with knowledge of the different types of error and their sources, it is possible to reduce and estimate the magnitude of the error effects. Although there are many sources of analytical errors, they can traditionally be classified into three major types: systematic or determinate errors, random or indeterminate errors and gross errors. Systematic errors cause the mean of a set of analytical data to differ from the accepted value, causing all the results of a series of replicate measurements to be too high or too low.
The presence of systematic errors will affect the accuracy of the analysis. Systematic errors originate from known sources or at least from sources that can be identified, and the magnitude of the systematic errors is reproducible from one measurement to another. Systematic errors can be classified into three types, according to their source: instrumental error, method errors and personal error.
Examples of glassware include pipets, volumetric flasks and burettes. These examples are illustrative, but not exhaustive. Systematic-method errors are due to limitations of the analytical method itself.
WHAT are sampling errors—and WHAT can we do about them? Part 1
Reactions and reagents are examples of this type of error, i. Common examples would be lack of specificity or curtailed performance of a reagent to perform its full role in a reaction. This is, for example, the case when decomposition of an unknown sample fails to happen due to a reagent in the reaction. Thus, during a titration the extra titrant needed to produce a change in colour indicator after the equivalence point is an example of this type of error.
Systematic-method errors are the most difficult to detect and correct because its correction will require a change of some, or all parts of the analytical method itself. Systematic-personal errors are, for example, due to poor attention to important or critical aspects of the analysis context by the analyst. This may include poor judgement, carelessness and even lack of training of the analyst.
Analytical bias, i. In analytical chemistry it is assumed that random errors cause analytical data to be scattered pretty much symmetrically around a mean value, and this error has the same probability of been positive or negative. The presence of random errors will affect the precision of the analysis. The sources of random errors are due to uncontrollable variables and because of the inability to identify their sources, they cannot be completely eliminated.
A plot of relative frequency vs deviation from the mean, for a large number of individual errors, is known as a Gaussian curve or Normal Error. A Gaussian distribution assumes that only random errors are present in the analysis, i.
This critical assumption allows an appropriate statistical treatment of the analytical data obtained that will facilitate evaluation of the magnitude of this error—which in turn allows a bias correction to be performed. The sources of gross errors are typically considered to be human errors; gross errors will manifest themselves as outliers in a series of replicate measurements.