Complete the following steps to interpret an individual value plot.

Examine the center and spread of the distribution. Assess how the sample size may affect the appearance of the individual value plot.

Identify the densest clusters of symbols. The densest clusters represent the most common values. Assess the spread of each group to understand how much your data varies. Hold the pointer over any point for a tooltip that describes the observation.

For example, in this individual value plot of the diameters of plastic pipes, the spread increases each week.

Investigate any surprising or undesirable characteristics on the individual value plot. For example, an individual value plot of hardness measurements from a shipment of ball bearings shows a wider than normal spread of values. An investigation revealed that a change in the ball bearing manufacturing process caused the increase in variability.

The sample size can affect the appearance of the graph.

For example, although these individual value plots seem quite different, both of them were created using randomly selected samples of data from the same population.

An individual value plot works best when the sample size is less than approximately 50. If the sample is too large, the data points on the plot may be too densely packed together and the distribution may be difficult to assess. If the sample size is greater than 50, consider using a boxplot or a histogram instead.

Skewed data and multi-modal data indicate that data may be nonnormal. Outliers may indicate other conditions in your data.

Determine whether your data are skewed. When data are skewed, the majority of the data are located on the high or low side of the graph. Skewness indicates that the data may not be normally distributed. Often, skewness is easiest to detect with a histogram or a boxplot.

These individual value plots illustrate skewed data. The individual value plot with right-skewed data shows wait times. Most of the wait times are relatively short, and only a few wait times are long. The individual value plot with left-skewed data shows failure time data. A few items fail immediately and many more items fail later.

If you know that your data are not naturally skewed, investigate possible causes. If you want to analyze severely skewed data, read the data considerations topic for the analysis to make sure that you can use data that are not normal.

Outliers, which are data valuse that are far away from other data values, can strongly affect your results.

On an individual value plot, unusually low or high data values identify possible outliers.

Hold the pointer over the outlier to identify the data point.

Try to identify the cause of any outliers. Correct any data entry errors. Consider removing data values that are associated with abnormal, one-time events (special causes). Then, repeat the analysis.

Multi-modal data have multiple clusters, also called modes. Multi-modal data often indicate that important variables are not yet accounted for.

For example, these individual value plots are graphs of the same data. The simple individual value plot has two clusters, but it is not clear what the clusters mean. The individual value plot with groups shows that the clusters correspond to two groups.

If you have additional information that allows you to classify the observations into groups, you can create a group variable with this information. Then, you can create the graph with groups to determine whether the group variable accounts for the peaks in the data.

To add a group variable to an existing graph, double-click a data representation in the graph and then click the Groups tab.

If your individual value plot has groups, assess and compare the center and spread of the groups.

Look for differences between the centers of the groups.

For example, this individual value plot shows the thickness of wire from three suppliers. The centers for wire thicknesses from each supplier seem to be different.

To determine whether a difference in means is statistically significant, do one of the following:

- Use a 2-sample t test if you have only two groups.
- Use a one-way ANOVA if you have three or more groups.

Look for differences between the spreads of the groups.

For example, this individual value plot shows the fill weights of cereal boxes from three production lines. Although the distributions of weight have almost the same center, the weights of some groups are more variable than others.

To determine whether a difference in spread (variance) is statistically significant, do one of the following:

- Use a 2 variances test if you have only two groups.
- Use a test for equal variances if you have three or more groups.