164 PART 4 Comparing Groups
the expected counts. The rigorous proof behind this is too complicated for most
mathophobes (as well as some normal people) to understand. Nevertheless, a
simple informal explanation is based on the idea that random event occurrences
typically follow the Poisson distribution for which the SE of the event count equals
the square root of the expected count (as discussed in Chapter 10).
Summarizing and combining scaled differences
For the upper-left cell in the cross-tab (CBD–treated participants who experience
pain relief), you see the following:»
» The observed count (Ob) is 33.»
» The expected count (Ex) is 25.8.»
» The difference (Diff) is 33
25 8
. , or 7 2. .»
» The SE of the difference is 25 8.
or 5.08
You can “scale” the Ob-Ex difference (in terms of unit of SE) by dividing it by the
SE measurement unit, getting the ratio Diff / SE 7 2 5 08
. / .
, or 1.42. This means
that the difference between the observed number of CBD-treated participants who
experience pain relief and the number you would have expected if the CBD had no
effect on survival is about 1.42 times as large as you would have expected from
random sampling fluctuations alone. You can do the same calculation for the other
three cells and summarize these scaled differences. Figure 12-4 shows the differ-
ences between observed and expected cell counts, scaled according to the esti-
mated standard errors of the differences.
The next step is to combine these individual scaled differences into an overall
measure of the difference between what you observed and what you would have
expected if the CBD or NSAID use really did not impact pain relief differentially.
You can’t just add them up because the negative and positive differences would
cancel each other out. You want all differences (positive and negative) to contrib-
ute to the overall measure of how far your observations are from what you expected
under H0.
FIGURE 12-4:
Differences
between
observed and
expected cell
counts.
© John Wiley & Sons, Inc.