Histogram

A histogram is one of the basic quality tools. It is used to graphically summarize and display the distribution and variation of a process data set. A frequency distribution shows how often each different value in a set of data occurs. The main purpose of a histogram is to clarify the presentation of data.

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What is the most common system response?
What distribution (center, variation and shape) does the data have?
Does the data look symmetric or is it skewed to the left or right?

Typical applications of histograms in root cause analysis include:
• Presenting data to determine which causes dominate
• Understanding the distribution of occurrences of different problems, causes,consequences, etc.

Weaknesses
There are two weaknesses of histograms that you should bear in mind. The first is that histograms can be manipulated to show different pictures. If too few or too many bars are used, the histogram can be misleading. This is an area which requires some judgment, and perhaps some experimentation, based on the analyst's experience.
Histograms can also obscure the time differences among data sets.
furthermore there is a loss of individual measurements due to grouping. Misrepresentation of data from groups that are too wide or too narrow.

Distributions to encounter:

Bi-Modal (double-peaked)
Distribution appears to have two peaks
• May indicate that data from more than one process are mixed together
o Materials may have come from two separate vendors
o Samples may have come from two separate machines
• A bi-modal curve often means that the data actually reflects two distinct processes with different centers. You will need to distinguish between the two processes to get a clear view of what is really happening in either individual process.