**Reproducibility in statistics**

Reproducibility in statistics refers to the measurement of variation that is taken repeatedly using the same measurement method and device. The observed variation in the item can be attributed to the variation of an item, rather than the measurement instrument. You need to be confident that your measurement system will deliver consistent and reliable results if you are to make data-based decisions. One measure of the trustworthiness of your measurement system is its reproducibility statistics.

Measurement system analysis (MSA) is a formal statistical study that determines if your measurement system can provide reliable data to enable you to make data-driven decisions. A Gage R&R Study is a statistical study that is used for continuous data. In reproducibility statistics, the R stands for repeatability and reproducibility statistics.

You will never know if your measurement system is accurate or precise. The measurement system will give you an estimate of the item’s value based on the natural variation and the variation within the measurement system.

The measure of how many people measure the same item using the same measuring device and method is called reproducibility in statistics. The amount of variation observed due to inter-person variation would be called reproducibility. Below is an illustration of what good and bad reproducibility might look like.

## Gage Repeatability and Reproducibility: Why are they important?

Gage Repeatability, Reproducibility, and Gage Repeatability measure the variability in measurements due to the measurement system. This variability is then compared to the total variability to determine how much variability the measurement system actually has. Gage R&R can be very helpful when new workers are assigned, tools are being used, or if there is a significant process change.

Imagine a situation in which our manufacturing process performance metrics show us a serious problem. It is a problem that we spend a lot on trying to fix and improve our manufacturing process’s performance. We would have seen serious deviations in measurement if we had spent more time focusing on gage repeatability and reproducibility statistics. The problem was not in the process, it was in how the measurements were taken. This would have saved you time, money, and stress, and even prevented it from happening.