Accuracy of Assessment
Accuracy, Precision, and Bias
When we
present an error (e.g. ± 5cm) with a measurement (e.g., 180 cm) it does not
mean a mistake, but rather due
to experimental limitations there is uncertainty of ± 5cm in the quoted value.
The term
accuracy refers to the closeness of a measurement
or estimate to the TRUE value.
The term precision (or variance) refers
to the degree of agreement for a series of measurements.
There are two
common measures:
Bias refers to the tendency of measures to
systematically shift in one direction from the true value and as such are
often called systematic errors. Such errors are
often caused by poorly calibrated instruments. In
contrast, random errors are produced by the
statistical fluctuations observed when measuring a quantity. These result in a
spread of observed measurements about a center value.
Expressing Uncertainty
Some things in life are relatively certain. If you buy a 6pack of soda, you
can expect to get 6 cans of soda (barring some unforeseen problem).
Thus, if you ordered 13 6packs of soda you would probably get 6 cans per
park plus or minus (+)
0 cans. Or, your average would be 6 +
0 cans of soda per pack.
However, few things in measuring vegetation are certain. We
commonly use averages to express what is called a "central tendency." In
the example of the target and arrows above, we are relatively accurate if the
average of our shots (i.e., the central tendency) is near the bulls eye.
An estimate of uncertainty (or, spread of data) is an expression of our
precision.
For example one might examine 50 plots and count the number
of sagebrush plants in each plot and calculate an average. The average number of
plants per plot might be 4.5 + 0.5 plants. The 0.5 represents the
precision through an estimate of standard deviation or standard error (For more
info on how to make calculations, visit College of Natural Resources
Student Learning Center  Statistics  Lesson 3). We will use
estimates of precision and spread of data throughout this course.
A NOTE OF CAUTION  avoid excessive units created by
calculators and spreadsheets. In the example above we measured 50 plots
and I noted that there were 4.5 + 0.5 plants/plot. Actually, when I
finished the calculations in my spreadsheet there were 4.423 plants per plot.
But, I made the informed decision to round to the nearest half plant (0.5
plant). The number 4.423 insinuates that I could recognized fractions of a
plant to 1/1000^{th} (i.e., 0.001). Sorry, I just can't envision
1/1000^{th} of a plant and I don't think it would be very useful to try to
make decisions based on 1000^{th}s of a plant. Especially because
I only measured 50 plots. Just because you can calculate numbers with many units
beyond the decimal doesn't mean you should. Here are a few guidelines:

Avoid rounding numbers while making calculations  you
can lose accuracy if you do.

When reporting numbers, round to the unit level that
you think you could realistically measure and recognize in nature.

Also, think about the unit level that would be useful
to make management decisions or conclusions about differences between sites.
