1. Check Cruising Social : Science by Kim Iles : SITCA

2. Social : Science
My life in academia ….

3. Ideal Field Crew attributes
Hyperactive
Obsessive Compulsive
Workaholic
Paranoid
but skilled, of course

4. How do we know the work is skilled?
We check it (or we better …)

5. What are we after ??
Sampling Error (in the BC Forest Act) is about getting a stable answer (effort).
Quality Control (not in the Forest Act) is about getting the correct answer (skill).
Shouldn’t check for both ?? Yes…

6. “Examples” vs. “Samples” Which do you have?
If you are not formally sampling, your numbers are only examples of the work. You cannot do valid math with such numbers.
It’s mainly about Training vs. Quality Control.

7. The social stuff counts
Work ethic
Safety attitude Social Issues,
Flexibility or Selection issues
Ability to learn and adapt
Daily Output

8. Checking their work … Do they know the rules? Do they apply the rules?
Training issues
Do they apply the rules?
Do they do it right ? Science issue.
Measurement issue.

9. For any sample
All items must have some chance of being sampled. “1 in 30”, for instance,
then that probability is used as a weight.
If the plots represent different amounts, that is also used as an additional weight.
Equal probability is not needed (or wanted)

10. Maybe the work is checked on …
10% on Monday (slow start)
20% on Tuesday (no problem)
Nothing on Wed (meeting day …)
Therefore … you have examples, not samples
and you cannot calculate correctly.
Low probability is OK … zero is fatal.

11. Random samples are not necessary, but the selection should not be predictable.
“The check cruiser is coming up the road …”
In a contest for advantage, random samples are often a safe procedure (hard to beat).

12. For training purposes … Choose the plots any way you like.
Take the cruiser along, of course.
For QC purposes – sample the plots.
Do all the work, independently.
The cruiser need not be there.
Compute your own results for comparison.

13. “Tic marks” are for training (social issue).
Differences are for quality control
Data entry and coding (for calculations)
Gross Volume (measurement issues)
Net volume (decay issues)
Value (species & quality issues)
The standard? (Your ability * ) ??

14. Different Time issues … (Statistical terms)
Scaling is a “hot deck” situation were you need to sample on the spot (or nearly).
Timber Cruising is a “cold deck” situation where you can revisit the plots over quite a time period.

15. Statistical bias comes from …
Wrong Selection (of the item checked)
This includes “no chance” of measurement.
The chance can be small – but not zero.
Wrong Measurements by the crew (and check-cruisers should also be checked).
Wrong Calculations (lack of weighting)

16. How accurate is your system?
This is scientific issue, not a social issue
It does, of course, have social implications … confidence, especially.
Here, the percentage or amount is the issue – so calculate it.

17. If some items are “too far” off ?
If an entire set of measurements are redone, and another check done on it.
Replacing one original measurement with the check cruise measurement is fraud. What about the other 50 items it represented?
Scalers do this all the time (at minimum, both numbers should be kept).

18. How is this data used? (sometimes)
Inventory records are corrected by production cruising.
Production cruising is corrected by the check cruising (as a percent). Raises the level of the work to work with more time (and talent) ,and better conditions.
Check cruisers are corrected by felled trees. Eliminating most assumptions about the trees (form, etc).
New $ values are assigned when they change. Updating the value results to the present.

19. The Message … How you feel about the results matters.
One way to instill confidence is social.
Another is scientific.
One is not a substitute for the other. Work up the data !!

23. ADD VALUE

24. And the Moral is … Sampling is Science
And it matters
Otherwise, this is just a Social exercise.
which is necessary – but not adequate.

25. Imagine the impression given … …and we risk a lot of your money ….”
“We don’t know how adequate our data is but … we’re really… like, happy.
…and we risk a lot of your money ….”
This is, in fact, very common.

27. One alternative.
Do it right and wrong once or twice, then show that the error of doing it wrong is not that much different – then stop worrying about it.
This is often done in statistics.

28. Thanks for your patience.