Non-Parametric Testing for Widget Defectivity: Sample Size Considerations

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SUMMARY

This discussion focuses on the application of non-parametric testing to assess the defectivity of widgets produced in batches of 25. The primary concern is determining an appropriate sample size for testing defect counts, especially when variance within batches is less than between batches. The consensus is that the complexity of real-world scenarios necessitates a tailored approach rather than relying solely on theoretical models. The importance of understanding the specific context and characteristics of the widgets is emphasized for accurate statistical analysis.

PREREQUISITES
  • Understanding of non-parametric statistical tests
  • Familiarity with defect count analysis in manufacturing
  • Knowledge of variance concepts in statistics
  • Experience with sample size determination techniques
NEXT STEPS
  • Research non-parametric tests such as the Mann-Whitney U test for defectivity analysis
  • Learn about sample size calculation methods for non-parametric tests
  • Explore variance analysis techniques in manufacturing quality control
  • Investigate case studies on defect count assessments in production environments
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Quality control analysts, manufacturing engineers, statisticians, and anyone involved in assessing product defectivity and implementing statistical quality control measures.

DeShark
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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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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.
 
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).
 
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.
 
How many parts make up a widget and what is the usual defect count?
 
Last edited:
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".
 

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