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165 STATISTICS
means giving a known, though not necessarily equal, chance of selection to
every token in the population. In the simplest case (called simple random sampling)
it means that the chance of selection of any token of our vowel in the
sample is not in any way connected to the selection of any other token in the
sample. In linguistics, what it means to have a random sample is frequently
fudged. Does it mean that no two tokens are produced by the same speaker? In
the simplest case it does, but this requirement is rarely met in linguistic sampling.
A thousand tokens from a single speaker clearly tell us less about the population
than 10 tokens from each of 100 speakers. And if we have 10 tokens
from 100 speakers who live in the same street then we’ll learn less than if those
100 speakers come from all over the city. There are statistical methods, some
quite complex, for taking such associations between sample members into
account when making inferences. However, all the linguist needs to keep in
mind is that although in general we learn more from larger samples, if the individual
responses are linked to each other in any way at all precision is lost.
So far, the assumption has been made that you just want to know something
about your sample as a homogeneous set: what the quality of a vowel is in
Birmingham, for example. But you may believe that speakers in one suburb
speak differently from speakers in another suburb, or that people over 190 cm
in height speak differently from speakers under 160 cm in height. In such cases
you are in effect dividing your survey population into a number of subpopulations,
and then comparing the subpopulations. If the subpopulations are large
you may be confident of finding enough people in a random sample (e.g. a
random sample of students in most subjects ought to net a large number of both
males and females, even if the proportions are not 50:50). However, sampling
for minority groups, such as people above 190 cm tall, requires a purpose-built
sampling scheme to make sure you get enough sample members from that subpopulation.
You might need, for example, to visit basketball clubs to find
people over 190 cm tall. Such an approach may complicate the analysis somewhat,
and statistical advice should be sought at the planning stage if you need
to design a complex sampling scheme. How you create a random sample of the
relevant population is an important matter, as is how you ensure a response
from your random sample.
Presenting the sample
Having taken a sample and derived such information as you require from that
sample, you are ready to describe the behaviour of that sample. Where your
sample is one of actual people, you will need to get those people to produce a
certain piece of information for you in a recordable way. Where your sample is
a number of words or sentences, you will need to analyse them in the way relevant
to your experiment (e.g. count how long the words are in letters and/or