- Published: 27 Apr 2019
Is your Data Reliable enough to do the Analysis?
Data – what we need to do the analysis? But how much we are sure that the data we have collected for analysis is reliable enough! The main agenda of this post is to discuss data reliability, it’s importance and how to achieve it.
Let’s start with a brief example, suppose during a clinical trial “If the data are not taken properly?” And proceed further for analysis. Then “what will be the consequences of it?” Clearly, it may fail later and results in loss of resources and time. Thus, the device measuring the host condition should produce reliable results.
Now, I would like to introduce the term called “Measurement system analysis (MSA)” which helps to determine the reliability of a data for analysis. You must be curious to know about it, right!
- Attribute data – When data collected are in the form of count or categorized in the form of group. For e.g. accept or reject, good or bad, and so on. In such scenario, we use Attribute agreement analysis.
- Continuous data – When data collected are in the form of fractional or decimal value. For e.g. weight, length, time, temperature, etc. In such scenario, we use a Gage R & R analysis.
Suppose in a wheel manufacturing company, operators use gage to measure the diameter of the wheel. Are we 100% sure that the collected data is measured correctly or not?
With reference from the Gage Run Chart – Three operators measure 10 wheels, three times per wheel randomly. Here, we can examine the differences in measurements between operators. We can see that Operator B does not measure consistently, and Operator C usually measures lower than the other operators.