Page 65 - Intermediate Statistics for Dummies

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Part I: Data Analysis and Model-Building Basics
Don’t put all your data into one basket!
An animal science researcher came to me one
made a terrible realization — all of his data came
time with a data set he was so proud of. He was
from exactly one cow. With no other cows to
studying cows and the variables involved in
compare with and a sample size of just one, he
helping determine their longevity. He came in
had no way to even measure how much those
with a super-mega data set that contained over
100,000 observations. He was thinking “Wow,
results would vary if he wanted to apply them to
another cow. His results were so biased toward
this is gonna be great! I’ve been collecting this
that one animal that I couldn’t do anything with
data for years and years, and I can finally have
it analyzed. There’s got to be loads of informa-
the data. After I summed up the courage to tell
him, it took a while to peel him off the floor. The
tion I can get out of this. The papers I’ll write,
moral of the story, I suppose, is to find a statisti-
the talks I’ll be invited to give . . . the raise I’ll
get!” He turned his precious data over to me
cian and check out your big plans with her
before you go down a cow path like this guy did.
with an expectant smile and sparkling eyes. But after looking at his data for a few minutes I
Getting Good Precision
Precision is the amount of movement you expect to have in your sample
results if you repeat your entire study again with a new sample. Low precision
means that you expect your sample results to move a lot (not a good thing).
High precision means you expect your sample results to remain fairly close in
the repeated samples (a good thing). In this section, you find out what preci-
sion does and doesn’t measure, and you see how to measure the precision of
a statistic in general terms.
Understanding precision from
a statistical point of view
You may think that precision means the level of correctness you have in your
statistical results. But precision only measures the level of consistency in the
results from sample to sample. Your results can be consistently correct or
consistently incorrect.
For example, a field-goal kicker on a football team may consistently kick the
ball two feet to the right of the goalposts every single time. Even though he’s
consistent, he never gets to score, because his results are systematically off
by the same amount each time. In other words, his results are biased, even
though they’re precise.