Treatment of Numerical Data - Exp 2 (dTf)

[robert zellmer]

I always get a number of questions about Exp 2 and what to do for the
average MW if two of the trials are close and one isn't.  Should you include
all three in the average in this case?

Sometimes the answer is pretty obvious.  If you have 150, 155, 220 hopefully
you realize something may be wrong with the 220 since the other two are so
close.   In this case the 220 would not generally wind up being included in
calculating the average MW.  In the discussion section one would list 
all three
MWs and the average and explain the one MW was left out of the calculation
of the average.  I suppose it could happen the 220 is the correct one 
and somehow
one got two wrong results out of three which were close but this would be
unusual (at least we hope).

Sometimes what to do isn't quite so obvious.  What if the results were 150,
160 and 190.  Should the 190 be included.  If you do the average rounds to
170 (2 s.f.).  If you don't it rounds to 160 (2 s.f.).  Which is 
correct?  I would
probably go with the 160.  However, to make a correct decision one needs to
do an error analysis and see if the 190 should be included in getting the
average.

I have a link under the "Laboratory" link.  This is a copy of Appendix D
in the current lab manual.  Here's the direct link,

*Treatment of Numerical Data (Error Analysis, sig. fig., graphing)* 
<http://chemistry.osu.edu/%7Erzellmer/chem1220/lab/App_F_1220_lab_manual.pdf>

The discussion about how to determine if a data point can be ignored in
such cases is discussed in Section III, "Reporting Results" on pages D-4
through D-5.

By the way, I can't tell you if leaving out one "bad" experimental 
result will lead
to a better or worse average.  That depends on your results and how careful
you were being.  It is quite possible for the two "good" results (they 
are very
close to each other) to actually be incorrect.  Generally, if you get 
two results
which are very close to each other and one which isn't one would expect the
two which are close to be better.  However, this isn't always the case.  
It is
quite possible you made a mistake and made that same mistake twice so the
two "good" ones aren't "good".  Besides, I don't know what any of the actual
true results are for any of the experiments.  Even if I did, I couldn't 
tell you
under penalty of death (well maybe not that severe a punishment but close).

For the post-lab data entry you have to enter your data as is (i.e. all 
three
MW values) and the average for all three (including the "bad" one). Then
in the data table in the report template, for the average list what you 
get from
just the two "good" MW values.  Discuss this in the Discussion section 
of the
report.  List all three MWs in the Discussion and the average based on 
just the
two good ones. Explain this was done and why.  Include a calculation in the
sample calculations showing the "bad" value could be left out.  You can find
how to do this at the link above (

You might wonder if you can ask for a manual regrade of the results grade.
Any requests for regrading the results grade results in a 50% reduction in
the score, meaning the maximum score you could get would be 7 pts rather
than 14 pts.  Also, your % error has to be pretty large before you don't get
any points.  Finally, 14 pts equates to less than 0.3% of your total course
score and this is the only lab for which your results are graded. I think
about the only reason to bother considering a request for a manual regrade
is if you get zero points due to calculation mistakes you made in the
on-line data entry which results in getting zero points for accuracy.

By the way, if you do make calculation mistakes for the data entry you
need to correct them and report the corrected values in the report or you
could have additional points deducted.  Your best defense against making
calculation errors for the data entry is do ALL the calculations and double
check them BEFORE doing the data entry.  Then it's pretty much just a
matter of entering the numbers you have.

Dr. Zellmer
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