FIRE EFFECTS GUIDE - National Wildfire Coordinating Group

1. It is essential to know what the questions are before sampling and
data analysis can be designed to obtain answers. Sampling and data
analysis must be objective driven. Further, objectives should be
developed with sampling and data analysis in mind so that the
objectives are reasonable, measurable, and lend themselves to
analysis.
2. Sampling design and intensity (number of observations) should be
determined before initiating any data collection to ensure sampling and
analysis procedures are appropriate. The number of required
observations depends on desired precision and confidence levels; for
most management purposes it is usually adequate to sample within 20
percent of the mean at the 80 percent confidence level.
3. Experienced statisticians should be consulted before data are
collected and after data are analyzed.
4. It is not necessary, nor feasible in most cases, to sample and analyze
data from every community on every wild or prescribed fire. Usually it is
better to do an adequate job on one community than an inadequate job
on two or more communities. Further, it is often possible to design a
series of prescribed fires with a sampling and data analysis scheme
such that one fire or one stratum is emphasized and the remainder are
spot checked. Biologically oriented statisticians can provide advice on
how best to sample and analyze data when time and funding are
constrained.
5. Inadequate sampling is often more expensive than excessive
sampling; optimum sampling is usually the most cost effective.
6. Replicates are necessary to determine "sample error," and untreated
"controls" are necessary to isolate fire effects from other effects.
7. After data are collected and analyzed, consider both statistical
significance and biological significance; it is possible to establish
statistical significance that has no biological significance.
8. Place measured trust in results of data analysis. An unexpected or
undesired result is not a valid reason for discarding results and
"massaging" the data. Data analysis should be used to enhance
understanding as well as provide support.