Advantages and disadvantages of Mean,Mean and Mode
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Statistical Language - Measures of Central Tendency
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What are the measures of central tendency?
A measure of central tendency (also referred to as measures of centre or central location) is a summary measure that attempts to describe a whole set of data with a single value that represents the middle or centre of its distribution.
There are three main measures of central tendency: the mode, the median and the mean. Each of these measures describes a different indication of the typical or central value in the distribution.
What is the mode?
The mode is the most commonly occurring value in a distribution.
Consider this dataset showing the retirement age of 11 people, in whole years:
54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60
This table shows a simple frequency distribution of the retirement age data.
Age
Frequency
54
3
55
1
56
1
57
2
58
2
60
2
The most commonly occurring value is 54, therefore the mode of this distribution is 54 years.
Advantage of the mode:
The mode has an advantage over the median and the mean as it can be found for both numerical and categorical (non-numerical) data.
Limitations of the mode:
The are some limitations to using the mode. In some distributions, the mode may not reflect the centre of the distribution very well. When the distribution of retirement age is ordered from lowest to highest value, it is easy to see that the centre of the distribution is 57 years, but the mode is lower, at 54 years.
54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60
It is also possible for there to be more than one mode for the same distribution of data, (bi-modal, or multi-modal). The presence of more than one mode can limit the ability of the mode in describing the centre or typical value of the distribution because a single value to describe the centre cannot be identified.
In some cases, particularly where the data are continuous, the distribution may have no mode at all (i.e. if all values are different).
In cases such as these, it may be better to consider using the median or mean, or group the data in to appropriate intervals, and find the modal class.
What is the median?
The median is the middle value in distribution when the values are arranged in ascending or descending order.
The median divides the distribution in half (there are 50% of observations on either side of the median value). In a distribution with an odd number of observations, the median value is the middle value.
Looking at the retirement age distribution (which has 11 observations), the median is the middle value, which is 57 years:
54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60
When the distribution has an even number of observations, the median value is the mean of the two middle values. In the following distribution, the two middle values are 56 and 57, therefore the median equals 56.5 years:
52, 54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60
Advantage of the median:
The median is less affected by outliers and skewed data than the mean, and is usually the preferred measure of central tendency when the distribution is not symmetrical.
Limitation of the median:
The median cannot be identified for categorical nominal data, as it cannot be logically ordered.
What is the mean?
The mean is the sum of the value of each observation in a dataset divided by the number of observations. This is also known as the arithmetic average.
Looking at the retirement age distribution again:
54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60
The mean is calculated by adding together all the values (54+54+54+55+56+57+57+58+58+60+60 = 623) and dividing by the number of observations (11) which equals 56.6 years.
Advantage of the mean:
The mean can be used for both continuous and discrete numeric data.
Limitations of the mean:
The mean cannot be calculated for categorical data, as the values cannot be summed.
As the mean includes every value in the distribution the mean is influenced by outliers and skewed distributions.
What else do I need to know about the mean?
The population mean is indicated by the Greek symbol µ (pronounced ‘mu’). When the mean is calculated on a distribution from a sample it is indicated by the symbol x̅ (pronounced X-bar).
Skewed distributions:
mark as BRAINLIEST
Australian Bureau of Statistics
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Statistical Language - Measures of Central Tendency
Menu
Understanding Statistics
Draft Statistical Capability Framework
Statistical Language
ABS Presents...Videos
Statistical Skills for Official Statisticians
A Guide for Using Statistics for Evidence Based Policy
Statistics - A Powerful Edge!
ABS Sports Stats
ABS Training
Image: Statistical Language
Image: Measures of Central Tendency Measures of Central Tendency
Recommended: First read Measures of Shape
What are the measures of central tendency?
A measure of central tendency (also referred to as measures of centre or central location) is a summary measure that attempts to describe a whole set of data with a single value that represents the middle or centre of its distribution.
There are three main measures of central tendency: the mode, the median and the mean. Each of these measures describes a different indication of the typical or central value in the distribution.
What is the mode?
The mode is the most commonly occurring value in a distribution.
Consider this dataset showing the retirement age of 11 people, in whole years:
54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60
This table shows a simple frequency distribution of the retirement age data.
Age
Frequency
54
3
55
1
56
1
57
2
58
2
60
2
The most commonly occurring value is 54, therefore the mode of this distribution is 54 years.
Advantage of the mode:
The mode has an advantage over the median and the mean as it can be found for both numerical and categorical (non-numerical) data.
Limitations of the mode:
The are some limitations to using the mode. In some distributions, the mode may not reflect the centre of the distribution very well. When the distribution of retirement age is ordered from lowest to highest value, it is easy to see that the centre of the distribution is 57 years, but the mode is lower, at 54 years.
54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60
It is also possible for there to be more than one mode for the same distribution of data, (bi-modal, or multi-modal). The presence of more than one mode can limit the ability of the mode in describing the centre or typical value of the distribution because a single value to describe the centre cannot be identified.
In some cases, particularly where the data are continuous, the distribution may have no mode at all (i.e. if all values are different).
In cases such as these, it may be better to consider using the median or mean, or group the data in to appropriate intervals, and find the modal class.
What is the median?
The median is the middle value in distribution when the values are arranged in ascending or descending order.
The median divides the distribution in half (there are 50% of observations on either side of the median value). In a distribution with an odd number of observations, the median value is the middle value.
Looking at the retirement age distribution (which has 11 observations), the median is the middle value, which is 57 years:
54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60
When the distribution has an even number of observations, the median value is the mean of the two middle values. In the following distribution, the two middle values are 56 and 57, therefore the median equals 56.5 years:
52, 54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60
Advantage of the median:
The median is less affected by outliers and skewed data than the mean, and is usually the preferred measure of central tendency when the distribution is not symmetrical.
Limitation of the median:
The median cannot be identified for categorical nominal data, as it cannot be logically ordered.
What is the mean?
The mean is the sum of the value of each observation in a dataset divided by the number of observations. This is also known as the arithmetic average.
Looking at the retirement age distribution again:
54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60
The mean is calculated by adding together all the values (54+54+54+55+56+57+57+58+58+60+60 = 623) and dividing by the number of observations (11) which equals 56.6 years.
Advantage of the mean:
The mean can be used for both continuous and discrete numeric data.
Limitations of the mean:
The mean cannot be calculated for categorical data, as the values cannot be summed.
As the mean includes every value in the distribution the mean is influenced by outliers and skewed distributions.
What else do I need to know about the mean?
The population mean is indicated by the Greek symbol µ (pronounced ‘mu’). When the mean is calculated on a distribution from a sample it is indicated by the symbol x̅ (pronounced X-bar).
Skewed distributions:
mark as BRAINLIEST
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thanks
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