What are some important differences between standard deviation and interquartile range?
The standard deviation takes into account all the values of a dataset, including any outliers. It is dependent on the mean, because the value is used to tell how much the data deviates from the mean of a dataset.
The standard deviation is also important when we need to utilize the variance of a dataset, which is necessary to do things like linear regression or other analysis of data.
Because the standard deviation is affected by skewed data, it can be more reliable when the data is normalized and not skewed.
The Interquartile Range tells us how spread the data is. The larger this value is, the more spread out the data is, and conversely, the smaller the value, the less spread the data is.
Unlike the standard deviation, however, it does not take into account all the values in the dataset, but mainly their positions when the data is ordered. It is not affected as much by outliers or data that is skewed or not normalized.
Ultimately, using both when analyzing data can sometimes be better than only using one value, and we can obtain more insight by observing both.