Your Reliability Engineering Professional Development Site
Chris and Fred discuss the difference between ‘confidence,’ ‘tolerance’ and ‘prediction’ intervals. Is there any point in understanding the difference between these concepts? … or is it something only statisticians find useful?
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Chris and Fred discuss what this term ‘confidence’ means … especially when it comes to statistics (and reliability)
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Chris and Fred discuss the terms ‘accuracy’ and ‘intervals’ … which are used a lot by reliability engineers. And how they relate to ‘confidence.’ So what to do any of these terms mean?
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Chris and Fred discuss questions that are easy to ask … but hard to answer. And these questions are often hard to answer because the person asking the question hasn’t thought about the REAL question they are trying to answer. So how many samples do I need for everything to be OK???
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Chris and Fred discuss what it means to be ‘confident.’ Is it the way you carry yourself? Is it being able to rely or trust on something? Or is it the probability that some variable quantity will be within two limits? More importantly … what does this mean in the domain of reliability and quality?
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Chris and Fred discuss what it means to have confidence in something. Confidence can mean all things to all people. How confident are you as a person? How confident are you in someone else? What confidence do you have in a conclusion? Statisticians and reliability engineers need to talk to each other where the term confidence has a clearly defined and fixed meaning. But does it? How confident are you in the results of a test? How confident are you in a test that has 75 data points versus one that has 70? How confident will your boss be in you when you present the outcomes of these tests? Have these tests shaken YOUR confidence? Best you listen to this podcast.
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Chris and Fred discuss data analysis … specifically the first question we ask before we help someone with their data analysis project. Chris always asks – what is the decision that this data analysis will support? And Fred always asks – where did this data come from? The reason these questions are important is that you need to know what information you need before you construct an analysis to get that information. And you need to be confident in the results. A single data set can potentially create multiple information sets. And this depends on how you construct the analysis. Which based on the decision. Listen to this podcast if you would like to learn more.
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