The DIP test, also known as the duality of pattern test, is a statistical method used to determine the presence of multimodality in a dataset. Multimodality refers to the presence of multiple modes, or peaks, in the distribution of data.

For example, consider a dataset of heights of people in a population. If the distribution of heights is unimodal, with a single peak at a certain height, it suggests that most people in the population are of a similar height. On the other hand, if the distribution of heights is multimodal, with multiple peaks at different heights, it suggests that the population has multiple subgroups of people with different heights.

The DIP test uses a combination of graphical and statistical methods to identify the presence of multimodality. The first step in the DIP test is to create a histogram of the data. The histogram shows the frequency of data points in different ranges of values, and helps identify the presence of multiple peaks in the distribution.

Next, the DIP test uses a statistical method called the Hartigans’ dip test to determine the presence of multimodality. The Hartigans’ dip test calculates a statistic called the dip statistic, which measures the difference between the maximum and minimum values in the distribution. If the dip statistic is less than a certain threshold, it suggests the presence of multimodality.

Here are two examples of the DIP test in action:

Example 1:

Consider a dataset of student grades on a test, with the following distribution:

60-70: 10 students

70-80: 15 students

80-90: 20 students

90-100: 5 students

If we create a histogram of the data, we can see that the distribution is unimodal, with a single peak at the 80-90 range. This suggests that most students scored between 80 and 90 on the test.

To further confirm the presence of unimodality, we can perform the Hartigans’ dip test. The dip statistic in this case would be calculated as the difference between the maximum and minimum values in the distribution, which is 100-60 = 40. Since this value is greater than the threshold, it suggests that the distribution is unimodal.

Example 2:

Now consider a different dataset of student grades on a test, with the following distribution:

60-70: 10 students

70-80: 5 students

80-90: 10 students

90-100: 15 students

If we create a histogram of the data, we can see that the distribution is multimodal, with two peaks at the 70-80 and 90-100 ranges. This suggests that there are two subgroups of students, with some scoring between 70 and 80 and others scoring between 90 and 100.

To confirm the presence of multimodality, we can perform the Hartigans’ dip test. The dip statistic in this case would be calculated as the difference between the maximum and minimum values in the distribution, which is 100-60 = 40. Since this value is less than the threshold, it suggests that the distribution is multimodal.

In summary, the DIP test is a useful tool for identifying the presence of multimodality in a dataset. By combining graphical and statistical methods, the DIP test allows us to determine whether a distribution has multiple peaks and identify the presence of multiple subgroups in a population.