Examine the distribution of your sample data, including the peaks, spread, and symmetry. Assess how the sample size may affect the appearance of the histogram.
Identify the peaks, which are the tallest clusters of bars. The peaks represent the most common values. Assess the spread of your sample to understand how much your data varies.
For example, in the following histogram of customer wait times, the peak of the data occurs at about 6 minutes. The data spread is from about 2 minutes to 12 minutes.
Investigate any surprising or undesirable characteristics on the histogram. For example, the histogram of customer wait times showed a spread that is wider than expected. An investigation revealed that a software update to the computers caused delays in customer wait times.
Some theoretical distributions, such as the normal distribution, are symmetric. Other theoretical distributions, such as the exponential distribution and the lognormal distribution, are right skewed. The Weibull distribution can be symmetric, right skewed, or left skewed. The skew of a Weibull distribution is determined by the value of the scale parameter. For more information, go to Weibull distribution.
A histogram works best when the sample size is at least 20. If the sample size is too small, each bar on the histogram may not contain enough data points to accurately show the distribution of the data. If the sample size is less than 20, consider using Individual Value Plot instead.
For example, although the following histograms seem quite different, both of them were created using randomly selected samples of data from the same population.
Multiple peaks (also called modes) often indicate that important variables are not yet accounted for. Outliers may indicate other conditions in your data.
Multimodal data have multiple peaks, also called modes. Multimodal data often indicate that important variables are not yet accounted for.
Outliers, which are data values that are far away from other data values, can strongly affect your results. Often, outliers are easiest to identify on a boxplot. On a histogram, isolated bars at the ends identify outliers.
Try to identify the cause of any outliers. Correct any dataentry errors or measurement errors. Consider removing data values that are associated with abnormal, onetime events (special causes). Then, repeat the analysis.
You can add a fitted distribution line to assess whether your data follow a specific theoretical distribution, such as the normal distribution. For more information, go to Customize the histogram and click "Distribution Fit".
Use Distribution Plot to create and compare theoretical distributions and to see how changing the population parameters affects the shape of each distribution.
Minitab uses the data in your sample to estimate the parameters for the fitted distribution line. For example, if you fit a normal distribution, Minitab estimates the mean and the standard deviation from your sample. Evaluate how closely the heights of the bars follow the shape of the line. Data that fit the distribution well have bars that closely follow the line.
 
 

In these results, the null hypothesis states that the data follow a normal distribution. Because the pvalue is 0.4631, which is greater than the significance level of 0.05, the decision is to fail to reject the null hypothesis. You cannot conclude that the data do not follow a normal distribution.
If your histogram has groups, assess and compare the center and spread of groups.
Look for differences between the centers of the groups. For example, the following histograms show the completion time for three versions of a credit card application. The center for each version of the credit card application is in a different location. The differences in the locations indicate that the mean completion times are different.
Look for differences between the spreads of the groups. For example, the following histograms show the weights of jars that were filled by three machines. Although the histograms have almost the same center, some histograms are wider and more spread out. The wider spread indicates that those machines fill jars less consistently.