What is Mode? ‘“ Determining Mode in Statistical Data
The Mode is referred to the number or the element that usually appears in a set of data of distribution of probability in statistics. In other fields, particularly in the field of education, the sample data are normally referred to as scores while the sample mode pertains to the modal score.

Similar to mean and median of statistics, the mode is used as a way to capture necessary facts and details regarding irregular variables or a populace in a common quantity. Mode generally differs from the mean and median, and can be very distinctive for strongly skewed or off balanced distributions.

The Mode is does not have to be unique, because the same maximum rate could be achieved at varying values. The most indefinite basis occurs in even distributions, where all of the values are equally probable.
If the value x is the mode of a discrete probability distribution where its probability mass function is taken from its highest value, this is the value that will be most probably be sampled.

In a mode of continuous probability distribution which is the value of x, where the probability density function reaches its highest value, the mode x can therefore be at its peak

As mentioned, the mode does not have to be unique, because the probability mass or density function may possibly reach its highest value at different points.

The description stated above shows that global maxima are the only ones that pertain to modes. It is a little confusing when a probability density function has several local maxima, all of the local maxima are generally referred to as modes of distribution while a constant or continuous distribution is referred to as multimodal and not unimodal.

When you are looking at symmetric unimodal distributions, like Gaussian distribution (the distribution where density function presents the well known “bell curve” when graphed), the mean (if given), median and mode are coinciding the other. While it is established that it is taken from a symmetric distribution, the sample mean is used as an approximate of the population mode.

In a set of data, the number or unit that appears more frequently than the others in a given set is the mode. In this example, [1,4,7,7,7,7,8,8,18], the mode is 7 where it has appeared more frequent than the others. However, in this set [1, 1, 4, 5, 5] there are two modes as this is called bimodal. Therefore in a set that shows more than three modes, this is called multimodal.

In this data set of continuous distribution, [0.936’¦, 1.212’¦, 2.431’¦, 3.669’¦3.987], the model is not useable in its physical form, given that each number will appear exactly once. The standard method is to use discretization in the data by transferring continuous samples into a discrete set of samples.

In this case this is done through giving a value on the frequency of intervals in equal distance, just like creating a histogram. Change the values with the midpoint of the distance they are given. The mode then is the value where the histogram shows its peak. Another optional approach is kernel density estimation, which is applicable to small, or medium sized samples.