When you come across a large data set, whether it’s survey results or just a homework problem, there are several ways you can try and describe the important aspects of the set. Each of these statistical values have pros and cons. This is a 3 part series highlighting the good, the bad, and the ugly of mean, median, and mode.
Here are links to the other two:
Mode Pros and Cons
All statistical calculations on large data sets involve some work. But with any statistical value, one measurement can’t truly identify all the important aspects of a given data set.
Mode Pros
- You do NOT have to put the data in order.
- No calculations necessary.
Mode Cons
- It is possible to have no mode, one mode, or many modes.
- Not a great descriptor of a data set, there is no guarantee that Mode will reflect the greater set.
Overall Mode involves the least work, and unfortunately that pretty much leaves it the least useful. It is possible that the mode and either the median or mean are similar, in which case mode is more relevant. But also in that case you have other values that are weighing the data.
Mode just means there is one specific data point that occurs more than the rest. It doesn’t suggest that it happens near a critical point, just that it happens. Sometimes the mode will be important, but just because it shows up often doesn’t MAKE it important.
Regardless, the mode is relatively easy to find. Although there can be none, one, or many, the mode can help you identify an important value of a large data set. Click here to see a step by step video and article on how to find the mode.