A simple summary using descriptive statistics is often the first step in investigating what data can tell you about a process under study during process improvement efforts. Questions like “Where is the central tendency?” or “Is the process skewed?” can be answered quickly by combining a histogram with summary statistics.

For example, imagine a manufacturing line producing a part whose dimension is measured in inches. The same approach applies to transactional processes such as call center wait times or number of users through a system.

Interpreting a Histogram + Key Metrics

A histogram visualizes the distribution of data: how many observations fall within each range. Alongside, descriptive statistics summarize the data using count, mean, standard deviation, and range. A five-number summary tells you where the quartiles lie. Confidence intervals let you estimate true population measures.

When the distribution appears bell-shaped and normal, you gain confidence that the data’s variance is acceptable. If it is skewed or shows multiple peaks, that suggests underlying subpopulations or shifts in process behavior.

Applying These Techniques in Process Improvement

Using histograms and descriptive statistics is not just academic. In Lean Six Sigma and continuous improvement, these tools frequently identify process instability, bias, or drift.

For instance, if a call center measures wait times across shifts, a histogram can show whether one shift consistently has longer delays. Descriptive statistics then quantify how far that shift deviates from the baseline. That insight enables targeted correction rather than generic fixes.

Best Practices and Limitations

• Use enough data points to ensure meaningful distributions.

• Combine histogram analysis with root cause tools like 5 Whys to dig deeper.

• Be cautious: histograms compress data into bins and may hide patterns if bins are too wide.

• Use confidence intervals and hypothesis tests to guard against overinterpreting random variation.