How to Reduce Sampling and get the same MTTF (Mean Time to Faliure)



Good day all,

I am working on a subject in regards to MTTF. So far, our standard work asks us to provide 36 units to put them in life test. this test is to determine the MTTF of the lot. This is for the new product development.

36 units is a large number, especially when the test is destructive and we are still in development.

I would like to find a way to demonstrate, right or wrong, that we can reduce the number of sample. Ideally it will be better for us to test 10 units. This will allow the development to try more configuration at iso-cost before getting deeper with the best configuration.

So, my question is I hope simple. Is there a way, with minitab or by anyother mean, to show that reducing the samples from 36 to 10 will give a similar result with a similar 95% CI? (The value 10 is arbitrary at this stage)

Thank you for your help on this problem.

Elsmar Forum Sponsor

Barbara B


with what method did you get a result of 36 samples? Could you pls give us some more information?

In general: If you test less than the number of pieces required (e.g. with Stat > Reliability/Survival > Test Plans > Demonstrate) under the same conditions which were used to calculate this sample size, you'll get a broader CI because the reliability of information is smaller with 10 parts than with 36.




Forum Moderator
In addition to what Barbara stated, the only way you can maintain the same confidence level while reducing the number of samples would be to extend the demonstration testing time, usually significantly.

Using Stat > Reliability/Survival > Test Plans > Demonstrate, you can enter both 36 and 10 (or 36 30 25 20 15 10) into the sample size field. Leave test time blank. The Session Window will show the test duration next to each sample size while maintaining the 95% confidence level.


Thank you for the comments.

I had a thought this night. In fact, what we want to do is to be bale to choose from 3-4 configuration.
Before we needed 36 (number chosen by our R&D people based on the minimum population to have a correct Cp, Cpk) samples, for each config.
Instead of having this, I will take these 3-4 config and only 10 sample for each config. I will then do comparison between them to understand which one is the best.
After that, I believe we still need to have a large population to have a good estimate of survival rate for the selected config.

As our products do not have any history (tailor made products) we cannot assume any distribution before we do the tests.

So, what do you think about the process stated above?

:thanx: for your feedback,

Barbara B


to compare different configurations you could take an arbitrary number of parts (e.g. 10) for each configuration and analyze the data with a methode called "regression with life data". There you can get informations about the reliability of the different configurations.

You can also take the study design one step further if the configurations can be described by several factors (like process time, material used, etc.) and these factors can be set up deliberately. Then the configurations are made up by a statistical approach and not by the engineers, they provide only the range for the different factors (region of interest). With this setup you can conduct a DoE and analyze the impact of factor settings on the reliability (design: factorial doe, method for analysis: regression with life data).

Either way you can evaluate which distribution is appropriate for the reliability data in the different configurations. For life data a Weibull distribution is a common one, but there are others in the field of reliability/survival which could be used (like Exponential, Lognormal, or others).


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