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7.
Product and Process Comparisons
7.2. Comparisons based on data from one process 7.2.4. Does the proportion of defectives meet requirements?
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| Derivation of formula for required sample size when testing proportions | The method of determining sample sizes for testing proportions is similar to the method for determining sample sizes for testing the mean. Although the sampling distribution for proportions actually follows a binomial distribution, the normal approximation is used for this derivation. | ||
| Minimum sample size |
If we are interested in detecting a change in the proportion
defective of size
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| Interpretation and sample size for high probability of detecting a change |
This requirement on the sample size only guarantees that a change of
size
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| Value for the true proportion defective | The equations above require that p be known. Usually, this is not the case. If we are interested in detecting a change relative to an historical or hypothesized value, this value is taken as the value of p for this purpose. Note that taking the value of the proportion defective to be 0.5 leads to the largest possible sample size. | ||
| Example of calculating sample size for testing proportion defective |
Suppose that a department manager needs to be able to detect any change
above 0.10 in the current proportion defective of his product line,
which is running at approximately 10% defective. He is interested in a
one-sided test and does not want to stop the line except when the
process has clearly degraded and, therefore, he chooses a significance
level for the test of 5%. Suppose, also, that he is willing to take a
risk of 10% of failing to detect a change of this magnitude. With these
criteria:
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