Graphs contain information and often tell a story. Our interpretation of the graphic can be aided or hindered by the design or style of the plot. Cleveland and McGill (1984) studied graphical perception and found the use of dot plots to aid viewers to understand the data’s message clearly.
The nature of a dot plot is like a bar chart, yet without the bars. Less ink, just a dot to indicate count or position along an axis permits conveying information simply. Due to its simplicity, it also permits adding additional information useful for comparisons or spotting trends, and more.
Uncertainty is another word for risk. Reliability uncertainty or risk is neither good nor bad, it just a bit unknown. Until we know the outcome, the eventual reliability performance, we will not know the impact.
So, how do we deal with reliability uncertainty? Will our product or system work as expected over time, or will it fail? Let’s examine a few of the common approaches in use and when and why the approach is effective.
As with any engineering challenge, we first need to recognize the problem is a challenge. We need to be aware of what we know and…
You may have heard of the 80/20 rule. The idea is that 80% of the wealth is held by 20% of the population. As an Italian economist, Vilfredo Pareto made this observation that became generalized as the
Pareto Principle: 80% of outcomes are due to 20% of causes
For field returns, for example, we may surmise that 80% of the failures are due to 20% of the components, for example. This principle helps us to focus our work to reduce field failures by address the vital few causes that lead to the most, or most expensive, failures.
The Pareto Principle…
How is it that some people continue to get better at managing meetings, designing complex test plans, making presentations, or solving problems? How in general do people improve their performance over time at something?
Peter Bregman in a Harvard Business Review article (November 09, 2019) titled “If You Want to Get Better at Something, Ask Yourself These Two Questions” outlined the process. The first question is: “Do you want to get better?” If you accept your current performance then there is no need to go further.
The second question Peter asks is: “Are you willing to feel the discomfort of…
Some time ago when talking with someone I just met, the conversation turned to what we did for a living. I mentioned being a reliability engineer, and his response: “Oh, yes, we do reliability”. Curious, as I’m not sure that I ‘do reliability’, we then talked about what he meant.
The conversation revealed that they had a list of tasks that they accomplished for each product under development. They did tests and reviews of the results. A lot of testing. They did FMEA and HALT. He believed the engineers did derating or stress/strength calculation. …
A common question when setting up a hypothesis test is concerning sample size. An example, might be: How many samples do we need to measure to determine the new process is better than the old one on average?
While this seems like a simple question, we need a bit of information before we can do the calculations. I’ve also found that the initial calculation is nearly always initiated a conversation concerning the balance of sample risks, the ability to detect a change of a certain size and the constraints concerning the number of samples.
We use a sample to estimate a parameter from a population. Sometimes the sample just doesn’t have the ability to discern a change when it actually occurs.
In hypothesis testing, we establish a null and alternative hypothesis. We are setting up an experiment to determine if there is sufficient evidence that a process has changed in some way. The Type II Error, β is a measure of the probability of not concluding the alternative hypothesis is true when in reality it is true.
The power, 1-β, reflects the ability of the sample to correctly lead us to the conclusion that…
In a recent blog post, Seth Goin discussed the need for ongoing investment to maintain infrastructure. Whether a road or building or even your own skills, it takes regular care to avoid system failures or obsolesce.
Seth opens his piece with:
If you want to see wisdom and maturity in action, look for someone (or a community) investing in infrastructure before it’s too late.
In the world of reliability engineering, we have the reliability maturity matrix that is an assessment tool. We can assess our design or system and how we go about making decisions concerning the reliability of the…
In hypothesis testing, we set a null and alternative hypothesis. We are seeking evidence that the alternative hypothesis is true given the sample data. By using a sample from a population and not measuring every item in the population, we need to consider a couple of unwanted outcomes. Statisticians have named these unwanted results Type I and Type II Errors.
Unknown to us, hence wanting to conduct a hypothesis test to learn something, the null hypothesis for the population under study may actually be true. On the other hand, the null hypothesis may not be true.
The view of the…
In the situation where you have a sample and would like to know if the population represented by the sample has a mean different than some specification, then this is the test for you. Oh, you also know, which is actually rather rare in practice, the actual variance of the population you drew the sample.
This test is often the first in the textbook that describes hypothesis testing. It is straight forward and provides a good foundation to apply other hypotheses tests with different circumstances.
A good practice when applying any statistical application is to consider the related assumptions. …
Reliability Engineering and Management Consultant focused on improving product reliability and increasing equipment availability.