Non-Parametric Testing for Widget Defectivity: Sample Size Considerations

In summary: There is no general answer to your question since it depends on the particular situation and the widget in question.
  • #1
DeShark
149
0
Hi all. I'm trying to come up with a way to determine if the defectivity of a particular widget is 'different' to the usual defectivity of a widget. The difficulty comes from the fact that widgets are made in batches of 25. We'd like to investigate any widgets which have a higher (or lower) defect count than should be expected.

Is it Ok to use a non-parametric test on the widget defect count, despite the fact that the variance of defectivity within a batch is less than the variance between batches. E.g. if only 3 batches have been made, what is the sample size? 75? This doesn't seem right to me...

Any insight or suggestion is very welcome. Thank you!
 
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  • #2
DeShark said:
I'm trying to come up with a way to determine if the defectivity of a particular widget is 'different' to the usual defectivity of a widget.

It isn't clear what that means. Taken literally it implies that there is a variable type of defectivity in widgets, but you didn't elaborate on what would make one type of defect different than another and without getting into those details, I don't think there is a general answer to your question.
 
  • #3
Sorry for not being clearer: each widget itself is made up of parts, each of which can be 'working' or 'defective'. The exact manner in which the parts are defective is unimportant; just the number of defective parts (or equivalently, the proportion of parts which is defective).
 
  • #4
I should have said:

I'm trying to come up with a way to determine if the defect count of a particular widget is 'different' to the usual defect count of a widget.
 
  • #5
How many parts make up a widget and what is the usual defect count?
 
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  • #6
My general advice is not to approach real life statistical problems as general problems! Textbooks pose problems involving widgets and being an expert on astronomy or strength of materials etc.doesn't help you solve them. In real life, if you have expertise in a situation, you shouldn't lobotomize yourself by thinking of it as a problem with "widgets".
 

Related to Non-Parametric Testing for Widget Defectivity: Sample Size Considerations

1. What are control limits of yield data?

Control limits of yield data refer to statistical boundaries that are used to monitor and analyze the variability of yield data. Control limits are typically placed at three standard deviations above and below the average yield, and they help determine if the variation in yield data is within normal limits or if there are any significant changes or patterns.

2. How are control limits calculated for yield data?

Control limits for yield data are calculated using statistical methods such as the mean and standard deviation. The upper control limit is calculated by adding three times the standard deviation to the mean, while the lower control limit is calculated by subtracting three times the standard deviation from the mean.

3. Why are control limits important for yield data?

Control limits are important for yield data because they help identify any significant changes or patterns in the data. By monitoring the variation in yield data using control limits, scientists can detect any potential issues or errors in the production process and make necessary adjustments to maintain the desired level of yield.

4. How do control limits differ from specification limits?

Control limits and specification limits are two types of statistical boundaries used for data analysis. Control limits are based on the natural variation of the data and are used for process control, while specification limits are based on customer expectations and are used for setting quality standards.

5. Can control limits of yield data change over time?

Yes, control limits of yield data can change over time. As the production process or environmental conditions change, the variability in yield data may also change. Therefore, it is important to regularly review and update the control limits to ensure they accurately reflect the current state of the production process.

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