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title: How Many Samples Do You Need to Be Confident Your Product Is Good?
description: How Many Samples Do You Need to Be Confident Your Product Is Good?
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# How Many Samples Do You Need to Be Confident Your Product Is Good?
Minitab Blog Editor | 7/12/2017
Topics: [Reliability Analysis](https://blog.minitab.com/en/blog/tag/reliability-analysis)
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How many samples do you need to be “95% confident that at least 95%—or even 99%—of your product is good?
The answer depends on the type of response variable you are using, [categorical or continuous](https://blog.minitab.com/blog/understanding-statistics/understanding-qualitative-quantitative-attribute-discrete-and-continuous-data-types?hsLang=en). The type of response will dictate whether you 'll use:
1. **Attribute Sampling:** Determine the sample size for a categorical response that classifies each unit as Good or Bad (or, perhaps, In-spec or Out-of-spec).
2. **Variables Sampling:** Determine the sample size for a continuous measurement that follows a Normal distribution.
The attribute sampling approach is valid regardless of the underlying distribution of the data. The variables sampling approach has a strict normality assumption, but requires fewer samples.
In this blog post, I'll focus on the attribute approach.
## Attribute Sampling
A simple formula gives you the sample size required to make a 95% confidence statement about the probability an item will be in-spec when your sample of size *n* has zero defects.
, where the reliability is the probability of an in-spec item.
For a reliability of 0.95 or 95%, 
For a reliability of 0.99 or 99%, 
Of course, if you don't feel like calculating this manually, you can use the **Stat > Basic Statistics > 1 Proportion** dialog box in Minitab to see the reliability levels for different sample sizes.
These two sampling plans are really just [C=0 Acceptance Sampling plans](https://blog.minitab.com/blog/applying-statistics-in-quality-projects/attribute-acceptance-sampling-for-an-acceptance-number-of-0?hsLang=en) with an infinite lot size. The same sample sizes can be generated using **Stat > Quality Tools > Acceptance Sampling by Attributes** by:
1. Setting RQL at 5% for 95% reliability or 1% for 99% reliability.
2. Setting the Consumer’s Risk (β) at 0.05, which results in a 95% confidence level.
3. Setting AQL at an arbitrary value lower than the RQL, such as 0.1%.
4. Setting Producer’s Risk (α) at an arbitrary high value, such as 0.5 (note, α must be less than 1-β to run).
By changing RQL to 1%, the following C=0 plan can be obtained:
If you want to make the same confidence statements while allowing 1 or more defects in your sample, the sample size required will be larger. For example, allowing 1 defect in the sample will require a sample size of 93 for the 95% reliability statement. This is a C=1 sampling plan. It can be generated, in this case, by lowering the Producer’s risk to 0.05.
As you can see, the sample size for an acceptance number of 0 is much smaller—in this case, raising the acceptance number from 0 to 1 has raised the sample size from 59 to 93.
Check out this post for [more information about acceptance sampling](https://blog.minitab.com/blog/applying-statistics-in-quality-projects/attribute-acceptance-sampling-for-an-acceptance-number-of-0?hsLang=en).
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