## IMPROVING RESULTS WITH A DIFFERENT KIND OF CRUISE
Cruises do not have to be of the same type to be combined. This general process is
useful whenever you happen to have (or need to have) two different The worst case nightmare with a 3P cruise is when you have not chosen any
"insurance trees" beforehand and fail to meet your sampling error. Perhaps you
wanted ±10% and got ±12% and there is a legal We have suggested some ways in past articles to insure that extra trees Could you install a few variable plots? There is a very powerful principle when dealing with several unbiased samples that says
"you can always combine them and come up with another unbiased sample. Not only that,
you can weight them differently, and thereby recognize the different
10% sampling error (on an average of
30,000 bf) and a VP cruise with a ±30% sampling error (on an average of 25,000
bf). How would you combine them? The standard way to do this is to "weight inversely proportional to the
variance." This means that you compute the total SE {1 / (±10%*30 mbf) and {1 / (±30%*25 mbf) The sum of the two weights is: (1/9 + 1/56.25) = 0.12889 So the (1/9) /0 .12889 = 0.86207 = 86.2% (1/56.25) / 0.12889 = 0.13793 = 13.8% We can now get an (86.2% * 30 mbf) + (13.8% * 25 mbf) = (25,862 bf + 3,449 bf) = so this is the proper estimate for the area, recognizing the quality of each of the two samples. This is pretty close to the 30,000 bf from the 3P cruise alone. Since the sampling
error of ±10% was small, that average was weighted How do we get a combined sampling error? That is pretty simple. (25,862 bf * ±10%) = ±7,759,034.5 bf taking the square root, we get:
The variable plot sample we used did reduce the sampling error from 10%, but only by 1/2% because there were very few plots in that sample. So this process can give us the answer when we combine two very different samples on
the same area, but
(1 / 10 Doing the math, we get: 0.01 - 0.006944 = 0.003056 = (1 / x (1 / 0.003056) = 327, and the OK, now we know what SE% the variable plot sample must produce. How many plots would that be? Suppose the CV of the variable plot was 55%. To get an 18% error would require: (55/18) So, you have your choice. Do you want to do a 100 acre 3P cruise again, or put in 10 variable plots and average the answers? For heavens sake put in a few extra plots on the second visit so we do not have to go out there a third time! This kind of computation is easy to put on a spreadsheet, and the calculations are a snap.
Suppose you were going to do a variable plot sample (55/10)2 = 30.25 or Now, you have a 3P sample already which you are going to use. Here is the critical
question -- Well, to have produced a sample of ±12% you would have had to install: (55%/±12%) so the sampling equivalent of that 3P sample you are going to use is 21 variable plots,
.Simple. |

*Originally published October 1998*