Histogram – Complete guide in detail

What is a Histogram and Why It’s Important in Quality Problem-Solving #

In manufacturing and quality engineering, data tells us the real story behind process variation.
A Histogram is a graphical tool that helps visualize how process data is distributed, and showing you whether your process is consistent, centered, or needs improvement.

It’s one of the 7 QC Tools, widely used to analyze measurement data such as dimensions, weights, or times. By showing how frequently values occur, a histogram helps engineers identify patterns, trends, and outliers that could indicate process issues.

In short: A histogram convert raw data into a visual chart of process behavior, allowing you to make informed, data-based decisions.

Concept Explanation: How a Histogram Works #

A histogram looks like a bar graph, but it represents continuous data divided into intervals or classes (called as bins).

Each bar shows how many data points fall within that range.

For example:

• A tall bar means many observations fall within that range.
• A short bar means fewer observations.
• The shape of the histogram (bell-shaped, skewed, or bimodal) reveals the process behavior.

Basic Components of a Histogram #

• X-axis: Represents data intervals (measurement ranges)
• Y-axis: Represents frequency (how often each range occurs)
• Bars: Show frequency distribution
• Center line: Shows average or target value
• Spread: Indicates variation within the process

A histogram provides quick insight into whether your process is:

• Stable or unstable
• Centered around the target
• Affected by special causes of variation

Step-by-Step: How to Create a Histogram? #

Here’s a practical guide to creating a histogram from your process data.

Step 1: Collect Data #

Gather continuous measurement data such as:

• Shaft diameter
• Paint thickness
• Welding strength
• Cycle time

Ensure at least 30–50 data points for a meaningful histogram.

Step 2: Determine Range #

Find the maximum and minimum values in your data.

Range = MaximumValue – MinimumValue

Step 3: Decide the Number of Intervals (Bins) #

Typically, use between 5 and 20 classes.
A quick formula:

Number of Classes = 1 + 3.3 × log_10​⁽ⁿ⁾

where n is the number of data points.

Step 4: Calculate Class Width #

Class Width = Range​ / Number of Classes

Step 5: Create Frequency Table #

Count how many data points fall into each class interval.

Step 6: Draw the Histogram #

Plot intervals on the X-axis and frequency on the Y-axis. Draw bars for each class interval, no gaps between bars, since it’s continuous data.

Real-World Example: Histogram in Automotive Manufacturing #

Let’s say you’re a quality engineer in an automotive supplier manufacturing piston rods.
You measure the rod diameter 50 times and plot the results on a histogram.

The histogram shows most values clustering around 20.05 mm, with slight spread on both sides, creating a bell-shaped curve.

Interpretation is:

• The process is centered around the target (good).
• Variation is within control limits.
• No unusual peaks or gaps (indicates stability).

However, if the histogram shows two peaks, it may mean two different machines or setups are producing parts, requiring process standardization.

Here are the two side-by-side histograms for your real-world example section:

• The left chart shows a Normal Process Distribution (Bell Curve) — representing a stable, centered process.
• The right chart shows a Bimodal Distribution — indicating variation between two machines or setups.

Advantages and Limitations of Histogram #

Advantages #

• Simple visual understanding of data variation
• Helps to identify process centering and spread
• Detects unusual patterns or defects
• Supports decisions in process improvement and SPC

Limitations #

• Doesn’t show time-based trends (use Control Chart for that)
• Doesn’t identify root causes (use Fishbone Diagram)
• Requires sufficient sample size
• Only works with continuous data

Best Practices & Tips for creating Histogram #

• Collect data under consistent conditions
• Use enough data points to ensure reliability
• Choose appropriate class intervals, not too few, not too many
• Always label axes and units clearly
• Combine histogram insights with other QC tools (e.g., Pareto Chart or Control Chart)

Common Mistakes and How to Avoid Them #

Histogram Template or Calculation Checklist #

Data Collection → Frequency Table → Histogram → Process Analysis

Summary / Key Points #

• Histogram is a visual tool to understand process variation.
• It helps identify whether a process is stable, centered, or shifted.
• Works best with continuous data.
• Combine it with tools like Check Sheets and Control Charts for deeper insights.
• Always interpret the shape to identify process improvement needs.

Frequently Asked Questions (FAQ) #

Now that you understand how to create and interpret a Histogram, try making one using your process data!

Download the free Histogram Template (Excel) or

Read the next article in this series: Control Chart – Complete Guide in Detail