Kvalitetsbegrepp och riktlinjer för kvalitets - Statistiska centralbyrån

1.2 Comprehensiveness
This quality component concerns how well the output from a "statistics system"
describes a subject matter field in its "vital aspects". (For instance, how well the
labour market statistics informs about vital aspects of the labour market.) However,
no statistics system will live up to what all users regard as all the vital aspects of the
subject matter field, but it can do so in a greater or less degree.
2 ACCURACY of the statistics
concerns agreement between statistics and target
characteristics
Usually there are so many disturbances in a statistical survey that the statistics do
not give exact values of the target characteristics, only estimates. Here, the lead star
is unbiased ("correct in average") estimates. Normally there is a discrepancy/deviation
("error") between a statistic and its target value. The smaller the
discrepancy is, the more accurate the statistic is. Of course users want discrepancies
to be small, preferably negligible.
Often, however, deviations are not negligible, and this is partcularly the case for
statistics from sample surveys. Then, at least statistically knowledgeable users want
numerical bounds for the deviations, referred to as accuracy measures or
uncertainty measures. The derivation of such measures is a somewhat intricate
matter, though, since the discrepancies depend on the target values, which are
unknown. (If they were known, a survey would be superfluous.) Statements about
accuracy of statistics inevitably fall in the category "statements concerning prevailing
uncertainty", a conceptually intricate topic. The common way to present
accuracy information is by uncertainty intervals, as indicated below.
It is likely that the value of the target characteristic lies in :
uncertainty interval = statistic ± margin of uncertainty.
Often such an interval is a confidence interval with a specified confidence level. If
this is 95 % , the interval encompasses the target value with a likelihood of 95 %.
Other accuracy measures, in essence equivalent to a confidence interval,. are : standard
deviation, coefficient of variation and relative margin of error. Sometimes
uncertainty intervals are based on special evaluations of the survey, subject matter
knowledge, experience, etc. Then one talks about judgement intervals.
A classification of "errors" which is helpful in some contexts is as random or systematic.
Random errors "fluctuate around 0", while systematic ones tend to "pull in
the same direction" (have the same sign). Systematic errors may lead to biased statistics.
2.1 Overall accuracy
A user is chiefly interested in how well he/she can rely on the statistic from an
overall point of view, i.e. to have a limit for the total deviation between the statistic
and the target value. Sometimes, but as an exception rather than a rule, the producer
can exhibit quantitative overall uncertainty intervals. Mostly, however, precise
information can only be given concerning the extent and consequences of certain
narrower sources of inaccuracy.
A comment on accuracy
Production of statistics can often be speeded up if standards of accuracy are lowered (by
accepting large non - response, incomplete editing, etc.). Production time and accuracy
are in conflict. In cases where fast statistics are judged to be important the dilemma is
often handled by presenting "preliminary" statistics in a first round, and later "definite"
statistics with better accuracy.
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