In my previous blog, I explained about some basic concepts of statistics. Today I am going to share some important concept that is what is the difference between mean and median because both mean, median measures the central value of the data. Then why we need two different measurements. Today teachers are not explaining this point they just say definitions and some basic examples. 

Explanation :-

We know that mean, measures the central value of the numerical data. Any one with basic knowledge in statistics knows how to calculate mean. Mean is also called as    “LOCATIONPARAMETER” why it is called as location parameter let us see this with an example if we want to find average marks of the  students. If we calculate the mean marks which can be interpreted as single representative value describing the location of marks of the students in the group. So it is called as location parameter in statistical language. 

Now we will discuss about median, same like mean it is measures the central value roughly speaking meadian divides the data into two equal halfs. 

Then whats the difference let us see this with an example consider the blood pressure data 

Data = (151, 142, 139, 131,146)

Find the mean for the above data Mean

 =(151+142+139+131+146)/5

= Approximately =142

 Calculate medain to do this First arrange the data in ascending order the find the middle one 

= 131,139,142,146,151

= middle value is 142 called as median. 

Due to device failure in BP machine  a false measurement is calculated that is 172, rather than 151.Then  

New data = 131,139,142,146,172 

Calculate mean, median for the new data. 

Mean = 146

Median = 142

The mean is changed and medain remains constant and unaffected by the false measurement. 

By this we can say mean is affected by the outliers and median is uneffected by outliers so median is called as robust against outliers. 

Which one is best :-

We cannot say which is best if we want to calculate standard deviation mean is the best, for categorical  data median is the best measure. But if the data contains more outliers mean gives false measures, so we can prefer to use median. According to the data we have use the measures. 

But in the real world applications we cannot apply these measures as we want. They should be applied according to the data and output we wanted to measure. We cannot do this without understanding the concept properly. The above examples makes you better to understand the concepts of mean, median. Before applying any measurements see the back ground of the data for better understanding. 

Thank you for  reading if you have any doubts please comment me in the  comment section. 

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