### Definition/Introduction

The mode refers to the most frequently occurring number found in a group of numbers. The mode is determined by collecting data to count the frequency of each result. The result with the highest count of occurrences is known as the mode of the set, which is also commonly referred to as the modal value. If multiple results all have the same high number of counts, then there can be multiple modes in the data set.

### Issues of Concern

The mode of a list or group of events is the most frequent output in that grouping. Therefore the mode will is the most abundant outcome. For example, if there is a set of numbers 1,2,2,2,2,2,2,3,3, the mode will be 2 as this is the number appears six times while the next most frequent number is 3, with only two occurrences. Besides working with numbers mode can also compare the number of occurrences of categorical lists.[1] If for example, a zoologist is listing the animals in the zoo, then the list may look like: bird, bird, tiger, tiger, tiger, lion, lion, lion, lion, tiger. This example is important as there is not just one mode but two separate modes. One can consider the tiger and lion the mode of this set since they both appear four different times, making this an example of how there can be multiple modes. In this way, one can use mode in various situations with either quantitative or qualitative categories, making the mode crucial to many data sets and a prominent statistical figure in many medical research papers.[2]

### Clinical Significance

The mode is one of the significant statistical values that go hand in hand with the mean and median of data sets to compare number sets or data for medical studies. Even with this connection, the mode has little correlation to the median and mean and therefore changes with the addition or subtraction of data points.[3][4] It is also an important distinction to make that there can be no mode for a data set if all the values only appear once. Here there would be no mode as no values appear most frequently. The last situation in which mode is useful is when there is a set of data that does not have a normal distribution, making it bimodal, with each peak representing a different mode of the data set.[5]