Individual Value Plot

In this article I'll look into "Individual value plot" and how can we interpret it to gain valuable intuition about our data distribution.

Individual Value Plot represents each value of data individually as a dot and it can be used to assess and examine the distribution.

When to use it?

If you have data distribution of 50 or fewer samples then you can use individual value plot, you can use individual value plot for more than 50 samples but it will not assess data easy as all data points would be clustered densely. To visualize data distribution having more than 50 samples you can use Boxplots and Histograms.

Interpreting the Individual Value Plot:

Individual value plot can provide us with intuition about the key characteristics of the data distribution.

Spread:

Individual value plot can give us information about the variability of the data, for that we have to look at the spread of the data, if data is spread over a wider range, this suggests that data sample vary significantly.

As we can observe in the graph that samples of week 3 vary significantly as compared to the other two distributions. 

Common Values:

Individual value plot also helps us to find common values in a distribution. Common values appear as a cluster in the graph. The densest cluster gives the most common values present in our data.

Sample Size:

Individual value plot works best if the sample size is less than or equal to 50. The appearance of the graph can be affected by the sample size, as the following image shows:

Evaluating and Comparing using Individual Value Plot

Centres:

If we have multiple groups in our plot, we can easily identify the difference in their centres. Following graph contains the data of 2 different distributions. We can observe that the centre of each distribution is different.

To check the statistical significance of this difference, we can use the following:

• Use 2 sample test, if you have only two groups
• Use ANOVA, if you have more than two groups.