Central Tendency

Describing the dataset

Central tendency is "the statistical measure that identifies a single value as representative of an entire distribution". It aims to provide an accurate description of the entire dataset. It is the single value that is most representative of the dataset.

The mean, median and mode are the three commonly used measures of central tendency.

1. Mode: is a value/range that occurred with the highest frequency.
2. Median: is the number that lies in the middle of a list of ordered numbers. The numbers may be in the ascending or descending order. such that there is an equal probability of falling above or below it. Simply put, it is the middle value in the list of numbers.
3. Mean: arithmetic average of a range of values or quantities, computed by dividing the total of all values by the number of values.

1. Mode

Mode is a value/range that occurred with the highest frequency.

Mode Characteristics

a. Uniform Distribution: NO MODE.

b. Distribution can have two modes like in "Foot Size" distribution for Men and Women has TWO modes, one for Men "Foot Size" and another for women. The distribution is a bimodal distribution.

c. Modes can be used to describe both catagorical and numerical data.

d. All scores in the dataset DOESNOT affect the mode. In the sense, if a new outliner value is added to the dataset, there is no influence on the mode value of the dataset.

e. Samples from the population WILL have DIFFERENT modes or NO mode depending on the sample values.

f. There is NO equation for the mode.

2. Median

Number that lies in the middle of a list of ordered numbers. The numbers may be in the ascending or descending order such that there is an equal probability of falling above or below it. Simply put, it is the middle value in the list of numbers.

For even samples : $$$$Median = \frac{X_\frac{n}{2}+ X_{\frac{n}{2}+1}}{2}$$$$

'X' is the sample in sample space
'n' is the number of samples in sample space

For odd samples : $$$$Median = X_{\frac{n+1}{2}}$$$$

'X' is the sample in sample space
'n' is the number of samples in sample space

Median Characteristics

a. All scores in the dataset DOES NOT affect the median. In the sense, if a new outliner value is added to the dataset, effectively there is NO change in the median value of the dataset.

b. The Median is robust in representing the the central/middle of the sample/population space. This is because outliners will have very less effect on median .

c. Samples from the population WILL have DIFFERENT medians as middle of a list of ordered numbers might vary between sample spaces of a population.

3. Mean

Arithmetic average of a range of values or quantities.

Sample space Mean: $$$$\bar{x}=\frac{1}{n}\sum_{i=1}^n x_i$$$$

'n' is the number of samples in sample space
$$$$x_i$$$$ is the ith sample space value

Population Mean: $$$$\mu=\frac{1}{N}\sum_{i=1}^N x_i$$$$

'N' is the number of samples in population space
$$$$x_i$$$$ is the ith population space value

Mean Characteristics

a. All scores in the dataset DOES affect the mode. In the sense, if a new outliner value is added to the dataset, effectively there is a change in the mean value of the dataset.

b. Mean of the sample can be used to make inferences about the population it came from.

c. The Mean can be misleading if the datasets has outliners. This is because outliners creates skewed distribution by pulling the mean towards outliners. This makes the mean lot less representative of middle of the data.

d. Samples from the population WILL have SIMILAR means (exception to outliners as explained in c above).

Summary

Central Tendency Equation? Changes with Sample values Affected by bin size? Affected by outliners? Easy to find on histogram
Mean Yes Yes No Yes No
Median Yes No No No No
Mode No No Yes No Yes

x = No / Not much