Learn Six Sigma in 4 weeks. Buy our Six Sigma Handbook 19.95$

- Six Sigma Tutorial
- Six Sigma DMAIC process
- Six Sigma Acceptance Sampling
- Sampling Plan Variation vs Lot Size Variation in Acceptance Sampling
- AQL Based Sampling Plans
- Decision Tree for Selecting Type of Variables in Sampling Plan
- FMEA – Failure Mode and Effects Analysis
- Types Of FMEA: Design FMEA (DFMEA), Process FMEA (PFMEA)
- The FMEA Quality Lever – Where To Put The Effort
- FMEA Quiz
- Six Sigma Confidence Intervals
- Confidence Limits
- Confidence Interval Formulas
- Z Confidence Interval for Means – Example
- t Confidence Interval for a Variance – Example
- Six Sigma Defect Metrics – DPO, DPMO, PPM, DPU Conversion table
- Fishbone Diagram – Fishbone Analysis
- Cost of Quality Defects and Hidden Factory in Six Sigma
- Pareto Analysis using Pareto Chart
- Six Sigma Calculators – DPMO, DPM, Sample Size
- How to select a Six Sigma project? Download selection grid template.
- How to create Six Sigma Histogram? Download Excel template
- Scatter Plots – Free Six Sigma Scatter Plot template
- How to create, use Six Sigma SIPOC tool? Download SIPOC Template
- Quality Function Deployment (QFD) – Download free templates
- What is Decision Matrix or Decision Making Matrix ?
- The nature of Process Variation
- What is RACI or RASCI Matrix/Chart/Diagram? Download free templates

Z Confidence Interval for Means applies to a mean from a normal distribution of variable data. Use the normal distribution for the confidence interval for a mean if the sample size n is relatively large (= 30), and s is known. The confidence interval (C.I.) includes the shaded area under the curve in between the critical values, excluding the tail areas (the a risk). The entire curve represents the most likely distribution of population means, given the sample’s size, mean, and the population’s standard deviation.

Here we are making an assumption that the underlying data we are working with is distributed like the bell curve shown. The most common confidence interval used in industry is probably the 95% confidence interval. If we were to use its formula on many sets of data from the population, then 95% of the intervals would contain the unknown population mean that we are trying to estimate. And 5% of the intervals would not contain the population mean. 2.5% of the time, the interval would be low, and 2.5% of the time, the interval would be too high. The probability is 95% that the interval contains the population parameter. The 95% value is the confidence coefficient, or the degree of confidence. The end points of the interval are called the confidence limits. In the graphic on the top, the endpoints are defined by

Learn all the Six Sigma Concepts explained here plus many more in just 4 weeks. Buy our Six Sigma Handbook for only 19.95$ and learn Six Sigma in just 4 weeks. This handbook comes with 4 weekly modules. Eeach module has around 250 powerpoint slides containing six sigma concepts, examples and quizzes.