greater than the undesirable level. If such a sampling plan could be employed, all lots of “bad”
quality would be rejected, and all lots of “good” quality would be accepted.
Unfortunately, the ideal OC curve in Fig. 15.3 can almost never be obtained in practice.
In theory, it could be realized by 100% inspection, if the inspection were error-free. The ideal
OC curve shape can be approached, however, by increasing the sample size. Figure 15.4 shows
that the OC curve becomes more like the idealized OC curve shape as the sample size
increases. (Note that the acceptance number c is kept proportional to n.) Thus, the precision
with which a sampling plan differentiates between good and bad lots increases with the size of
the sample. The greater is the slope of the OC curve, the greater is the discriminatory power.
Figure 15.5 shows how the OC curve changes as the acceptance number changes.
Generally, changing the acceptance number does not dramatically change the slope of the OC
curve. As the acceptance number is decreased, the OC curve is shifted to the left. Plans with
smaller values of c provide discrimination at lower levels of lot fraction defective than do
plans with larger values of c.
Specific Points on the OC Curve. Frequently, the quality engineer’s interest
focuses on certain points on the OC curve. The supplier or consumer is usually interested in
knowing what level of lot or process quality would yield a high probability of acceptance. For
example, the supplier might be interested in the 0.95 probability of acceptance point. This
would indicate the level of process fallout that could be experienced and still have a 95%
chance that the lots would be accepted. Conversely, the consumer might be interested in the
other end of the OC curve. That is, what level of lot or process quality will yield a low probability
of acceptance?