Most of your process data is not possible to collect. You will therefore take samples. If you used the same sample sampling technique, the confidence level is the percentage of time that repeated instances would be expected to approximate the first sample.
A confidence level of 100% means that you are 100% certain repeated samples will yield the same results. If your confidence level is 0%, it means that you are not confident repeated samples will produce the same results. For most business applications, you’ll aim for confidence levels of 90%, 95%, or 99%.
Because it affects your sample size , and confidence interval, the choice of confidence level is crucial. Your sample size should be larger if you are more confident. The confidence interval will also increase the more confident you are.
You can be confident that you can make a statement about your process if you have all of the process data available. However, the confidence you have in your process will drop once you begin taking samples. Let’s look at how confidence level can be used to describe the likelihood that you are able to say something about your process.
What is confidence level?
If we use a sample to gain insight into a whole population, whether it’s people or products made in factories, we run the risk of the sample group not accurately reflecting the entire population. We need confidence intervals.
Confidence intervals are used to determine if the sample group represents the entire population. You can think of it this way: If you draw the same-sized sample group hundreds upon hundreds of times, and do the same measurements as before, certain percentages of confidence intervals within those sample groups will contain population mean.
A confidence interval is a range or set of values. You can calculate a percentage of certainty that the average of the population is within this range for any sample taken from that population.
A case study from the industry to illustrate confidence levels
One warehouse manager was reviewing data about shipping efficiency. The warehouse manager had been collecting data for the last month. She wanted to know what range of values she could expect if she began to collect samples over time. The concept of confidence intervals and levels was explained by her Black Belt, (BB). Here are her calculations.