Page 63 - Intermediate Statistics for Dummies

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Part I: Data Analysis and Model-Building Basics
Designing the data-collection instrument: Poorly designed instruments
(including surveys) can result in inconsistent or even incorrect data.
For example, a survey question’s wording plays a large role in whether
or not results are biased. A leading question can make people feel like
they should answer a certain way. For example: “Don’t you think that
the president should be allowed to have a line-item veto to prevent gov-
ernment spending waste?” Who would feel they should say no to that?
Collecting the data: In this case, bias can infiltrate the results if some-
one makes errors in the recording of the data or if interviewers deviate
from the script.
Deciding how and when the data is collected: The time and place you
collect data can affect whether your results are biased. For example, if
you conduct a telephone survey during the middle of the day, people
who work from nine to five aren’t able to participate. Depending on the
issue, the timing of this survey could lead to biased results.
Bias can creep into a data set very easily. The best way to deal with bias is to
avoid it in the first place. You can do this in two major ways:
Use a random process to select the sample from the population. The
only way a sample is truly random is if every single member of the popu-
lation has an equal chance of being selected. Self-selected samples aren’t
random.
Make sure that the data is collected in a fair and consistent way. Be
sure to use neutral question wording and time the survey properly.
Settling the variance controversy:
The battle of n–1 versus n
Not all statistical formulas are free of bias. In other words, some statistics
have good characteristics (like offering great precision) and some not-so-
good characteristics (like not giving the best possible result in all situations).
Statisticians definitely prefer statistics that are both precise and unbiased,
and the techniques you find in this book have both qualities. However, pre-
cise and unbiased statistics doesn’t always happen naturally; sometimes the
basic idea requires a little tweaking to get a statistic that actually meets the
standards of the statistical powers that be (of which I am not one). The clas-
sic example of this need to fine-tune is the formula for the variance of a data
set, which I describe in the following section.