One of the first steps in choosing the right hypothesis test, and for the Lean Six Sigma Methodology in general, is ensuring whether or not the data is normal. Those new to hypothesis testing tend to assume data normality, as that simply makes data analysis easier. However, there's danger in these assumptions as certain projects may involve atypical data (like process cycle-time reduction). Normal data has normal variation and generally takes the shape of a bell curve. This curve represents the data's central tendencies. The properties of this type are such that those working with it may use a probability plot for actual verification that it is distributed in a normal way. This tool is based on statistical testing.
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Data produced by this probability plot, if the results are considered normal, will follow a straight line. This indicates that as the value increases, so does the percentage of total data that falls within that particular range. If anywhere between 80-90 percent falls between these lines, it is considered normal. The probability plot serves as the first step in deciding which hypothesis testing is indicated.
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