**Sampling 3: The effect of bias in samples**

Daniel Gile

September 8, 2006

Samples are
selected to be representative of populations. However, natural variability
within the population makes it likely that with respect to the feature(s) in
which investigators are interested, any sample will deviate to some extent from
the population. If the sample is chosen *at random* in the strict sense of
the word (that is, every unit in the population has the same probability of
being selected into the sample), such *sampling error* will also take
random values. It can be reduced by increasing sample size, and what is more,
it can be measured probabilistically, thus giving indications of the magnitude
of potential differences between population characteristics and sample
characteristics. In other words, sampling error limits the accuracy of
inferences about the population from a sample – but does not challenge their
validity.

A different type of error may arise
from sampling procedures which make the selection of units with certain
characteristics more likely than the selection of other units. For instance, in
a study of multilingualism in interpreters, if all subjects are sampled in *bias* will have been introduced which will result in values
in the sample deviating systematically – and not at random – from values in the
population.

Note that bias will not go away if
sample size is increased. Whether 10, 20, 30 or 100 West-European interpreters
are included in the sample, the mean number of working languages will remain
close to 3. Similarly, whether 10, 20, 30 or 100 East-Asian interpreters are
included in the sample, the mean number of working languages will remain very
close to 2.

Another problem with bias, a more
fundamental one, is that contrary to random sampling error, it cannot be
estimated. This means that if bias is present in the sample, it is difficult to
draw conclusions about the population, except in the case where the direction
of bias is known: or instance, if it is known that Japanese interpreters tend
to have more working days per year than interpreters in all other parts of the
world, if the mean number of working days per year in a Japanese sample is 125,
the one inference that can be made is that the mean number of working days per
year for the population of interpreters worldwide is less than 125.

Bias is therefore an important
challenge in research, and the risk of bias in every study should be considered
and fought.