The formula to calculate the Acceptance Sample Size (S) is:
\[ \text{S} = \frac{L \times A}{100} \]
Where:
Let's say the lot size (L) is 1000 units and the acceptance number (A) is 5. Using the formula:
\[ \text{S} = \frac{1000 \times 5}{100} = 50 \]
So, the acceptance sample size (S) is 50 units.
Definition: A sample size calculator helps determine the number of observations or replicates needed to achieve a desired level of accuracy.
Formula: \( n = \frac{Z^2 \cdot p \cdot (1 - p)}{E^2} \)
Example: \( n = \frac{1.96^2 \cdot 0.5 \cdot (1 - 0.5)}{0.05^2} \)
Definition: The acceptance number in sampling is the maximum number of defective items allowed in a sample for the lot to be accepted.
Formula: \( c = \text{Maximum number of defects allowed} \)
Example: If the acceptance number is 2, then up to 2 defective items are allowed in the sample for the lot to be accepted.
Definition: An approximate sample size calculator estimates the number of samples needed based on desired confidence level and margin of error.
Formula: \( n = \frac{Z^2 \cdot p \cdot (1 - p)}{E^2} \)
Example: \( n = \frac{1.96^2 \cdot 0.4 \cdot (1 - 0.4)}{0.06^2} \)
Definition: Lot acceptance testing determines the sample size needed to decide whether to accept or reject a lot based on the number of defects found.
Formula: \( n = \frac{N \cdot p}{A} \)
Example: \( n = \frac{1000 \cdot 0.02}{2} \)
Definition: Calculating sample size involves determining the number of observations needed to achieve a desired level of accuracy and confidence.
Formula: \( n = \frac{Z^2 \cdot p \cdot (1 - p)}{E^2} \)
Example: \( n = \frac{1.96^2 \cdot 0.3 \cdot (1 - 0.3)}{0.04^2} \)
Definition: Estimating sample size involves calculating the number of observations needed to achieve a desired level of accuracy and confidence.
Formula: \( n = \frac{Z^2 \cdot p \cdot (1 - p)}{E^2} \)
Example: \( n = \frac{1.96^2 \cdot 0.25 \cdot (1 - 0.25)}{0.05^2} \)
