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Dealing with Small Sample Sizes
Accendo Reliability Webinar Series
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Methods for Analyzing Small Sample Sizes
This chapter explores effective methodologies for analyzing small sample sizes in experiments, with a focus on Bayesian analysis and the bootstrap method. It highlights the advantages of these approaches in providing accurate variance estimates and invites listeners to engage with the hosts for further guidance and future webinar topics.
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Dealing with Small Sample Sizes
podcast episode with speaker Fred Schenkelberg
It's rare to have sufficient samples for all the desired reliability work. It's common to have very few samples. So, how does one manage to learn what is necessary to learn from the few samples? How can you maximize the value of reliability work in the scant sample situation?
Let's discuss a few approaches and a process that enable you and your team first to have the right amount of samples. And what can be done when there are too few samples available? The trick is to start with what future decisions need better information and not start with how many samples you have.
There are three strategies to deal with small sample sizes. First, focus on the critical information only samples can provide and how valuable that information is to the team. Second, use other methods to create the needed information without using samples. Third, organize the various testing and measurements of the sample allocated to reliability testing along with samples or units assigned to other sections of the team to extract as much information as possible.
This Accendo Reliability webinar was originally broadcast on 9 January 2024.
Related content:
ALT Sample Size episode
Reliability and Sample Size episode
Determine Success Testing Sample Size article
Three Elements of Sample Size Concerning Decision Making article
Three Considerations for Sample Size article
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