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Measures of Central Tendency

A detailed overview of the measures of central tendency, specifically, the mean, median, and mode.

Author:Author ImageKyle Patel

Edu Level: Unit1

Date: Sep 26 2025 - 3:12 AM

⏱️Read Time: 8 min



Measures of Central Tendency

Throughout your journey in statistical analysis, you will encounter a variety of measures that are utilized to make inferences from a data set. The most simple of these are noted as the measures of central tendency, which inherently describe the center or most likely value of a data set.


These are:

  1. Arithmetic Mean

  2. Median

  3. Mode


1. Arithmetic Mean

The arithmetic mean, or mean, is the average of the total sum of all the data values in a data set. It is important to note that this measure essentially incorporates every single value of a data set, excluding none. The sample arithmetic mean is denoted by the symbol x¯, whereas the population arithmetic mean is denoted by the symbol X¯.

The arithmetic mean can be calculated as such:

Ungrouped Data

Mean, x¯=xn

In which,

n=total number of data valuesx=a single data valuex=sum of all data valuesx¯=mean of the data set

For example, given the data values: 3,4,10,12,15,18.

In this case, the total number of data values, n=6. As there are six countable values amongst the raw data values.

As well as,

The total sum of all the data values, x=62.

Hence,

Mean, x¯=626Mean, x¯=10.3 (to 3 s.f.)

Grouped Data

Mean, x¯=fxf

In which,

f=the total frequency of the data valuesx=a single data valuefx=sum of all data values multiplied by frequencyx¯=mean of the data set

For example, given:

[ Marks Frequency, f1103203053040240504 ]

Whereby, we must then calculate the following: fx and f.

This can be done by calculating the midpoint, x which is found by:

Midpoint, x=UCBLCB

In which, UCB=the upper class boundaryLCB=the lower class boundary

Furthermore, it is optimal for the resulting calculations to be added to the table:

[ MarksFrequency, fMidpoint, xfx11035.516.520305251253040235704050445180f=14fx=391.5 ]

From the table, we can see that: fx=391.5 and f=14.

Hence,

Mean, x¯=391.514Mean, x¯=27.96Mean, x¯=28.0 (to 3 s.f)

2. Median

The median of a data set is the middle of center value. It is not 50 of the data set but rather it is the 50th percentile of the data set. This means that the median is the value at which 50 of the data lies below it. In order to calculate the median of a data set, the data must be arrayed.

Ungrouped Data

Median (Q2)=n+12

In which,

n=total number of data values

For example, given the data set: 3,7,4,10,15,12,18. We are to find the Median (Q2).

Firstly, we must array the data set as such: 3,4,7,10,12,15,18.

In this case, the total number of data values, n=6. As there are six countable values amongst the raw data values.

Secondly, we calculate the position of the median:

Median, (Q2)=n+12Median, (Q2)=6+12Median, (Q2)=72Median, (Q2)=3.5th position

Since the Median, (Q2)Z+, we find the values at the 3rd and 4th positions and take the average of it. If the Median, (Q2)Z+, the value at that position would be taken.

Finally, from our data set,

Median, (Q2)=7+102Median, (Q2)=172Median, (Q2)=8.5

Grouped Data

Utilizing Linear Interpolation,

Median (Q2)=l+(n2Ff)×h

In which,

n=total number of data valuesl=lower limit of the median classF=cumulative frequency of classes preceding the median classf=frequency of the median classh=class width

Utilizing graph estimation for continuous data,

Median (Q2)=n2

For example, given the table:

[ Mass, gFrequency, f010210205203083040440501 ] We must identify the Median class utilizing the previous method:

Median, (Q2)=n+12Median, (Q2)=20+12Median, (Q2)=212Median, (Q2)=10.5th position

Hence, the median class is: 2030. This was found by cumulatively adding up the frequencies until the 10.5th location is determined to fall within a respective class.

Now, n=20l=20F=7f=8h=10

Finally, we can apply these values to the formula given above:

Median (Q2)=20+(20278)×10Median (Q2)=23.75Median (Q2)=23.8 (to 3 s.f.)

3. Mode

The mode of a data set is the most frequent or repeated value. In any given data set, there can be multiple modes, no mode or a singular mode. Statistically, the mode simply gives us an idea of the data value that is most picked. For example, the most popular colour, car, brand, chips etc.

Unrouped Data

As aforementioned, the mode is simply the most frequent value.

Therefore, for example, for a given data set: 3,3,4,3,5,6,3,4,3.

The mode can be identified to be 3. This is because it was repeated 5 times in comparison to the other values in the data set.

Grouped Data

Mode=l+(f1f02f1f0f2)×h

In which,

f1=frequency of the modal classf0=frequency of the class preceding the modal classf2=frequency of the class after the modal classl=lower limit of the modal classh=class width

For example, given the table:

[ Age, yearsFrequency, f01041020122030330406 ] Firstly, the modal class can be easily identified as it is the class with the highest frequeuncy. Therefore, the modal class can be denoted as: 1020.

Now, from the table:

f1=12f0=4f2=3l=10h=10

Therefore, applying the formula stated above:

Mode=10+(1242(12)43)×10Mode=14.70588...Mode=14.7 (to 3 s.f.)

Exercise

Try:

  1. Calculate the mean and median of the data set: 3,10,22,44,31,25,10,12,13,41,24,26,32,40,150,198,210.

  2. The heights of students of Remuria Krai Secondary School were recorded in the table designated below:

[ Height, cmFrequency, f052051015101518152032202517 ]

Calculate: (i) Mean, (ii) Median, (iii) Mode

  1. The number of days that 100 employees were early for work in the last financial period is displayed below:

[ Number of Days EarlyFrequency, f0158162016213024415026617026 ] Calculate the (i) mean, (ii) median and (iii) modal number of days that the workers were early. (iv) Describe an advantage of the mean and median.

About Kyle Patel

Kyle Patel is a resilient and academically focused individual currently studying in Naparima College at the Upper Six level in the following subjects: Applied Mathematics, Pure Mathematics, Information Technology and Computer Science. Read More

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