Using Descriptive Statistics

Descriptive statistics summarize data in a way that reveals patterns, central tendencies, and variation. They provide a clear, concise picture of the process and help teams understand the behavior of the data. Descriptive statistics are essential for effective analysis and meaningful improvement. 

The most common descriptive statistics include measures of central tendency (mean, median, mode) and measures of variation (range, variance, standard deviation). These metrics help teams understand the typical performance of the process and the amount of variation that exists. 

Descriptive statistics also include measures of shape, such as skewness and kurtosis. These metrics help teams understand whether the data is symmetrical, skewed, or heavy‑tailed. Understanding the shape of the data is important for selecting the appropriate statistical tests and interpreting results accurately. 

Visual tools such as histograms, box plots, and scatter plots complement descriptive statistics by providing a visual representation of the data. These visuals often reveal patterns that raw numbers hide. 

Descriptive statistics are not just numbers—they are insights. They help teams understand the behavior of the process, identify opportunities for improvement, and make informed decisions. When teams use descriptive statistics thoughtfully, they gain a deeper understanding of the process and build a strong foundation for the rest of the Measure phase.