What Is the Median?
- The term median refers to a metric used in statistics. It is the middle number in a sorted ascending or descending list of numbers and can be more descriptive of that data set than the average. It is the point above and below which half (50%) of the observed data falls, and so represents the midpoint of the data. If there is an even amount of numbers in the list, the middle pair must be determined, added together, and divided by two to find the median value.
Advantages of Median Reports :
- Simple: It is one of the simplest measure of central tendency. It is quite easy to compute and simple to understand.
- Unaffected by Extreme Values: The extreme values do not effect the median of a series. For example, median of 20, 25, 30, 35, and 120 is 30; however, its mean is 46. With this, we can see that the median is not affected by the extreme value; i.e., 120, and is thus a better average.
- Informed Decision-Making: By providing a clearer picture of typical values, median reports help stakeholders make informed decisions based on the most representative data points rather than being influenced by extreme values.
What Is a Percentile in Statistics?
A percentile is a term that describes how a score compares to other scores from the same set. While there is no universal definition of percentile, it is commonly expressed as the percentage of values in a set of data scores that fall below a given value.
Advantages of Percentile Reports :
- Relative position: Percentiles provide a standardized way to understand the relative position of a specific value within a dataset. By comparing a value's percentile to the rest of the dataset, it allows for easy interpretation of how that value compares to others in terms of magnitude or rank.
- Robustness to extreme values: Percentiles are robust to extreme values or outliers in the dataset. Since percentiles focus on the position of values rather than their exact values, extreme values have minimal impact on the percentile calculations. This makes percentiles useful for analyzing data that may contain outliers or extreme observations.
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Conclusion:
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