Definition and Detection Methods
An outlier is an observation that lies significantly far from the bulk of a dataset. Common detection methods include flagging values beyond 1.5 times the interquartile range (IQR) or those more than three standard deviations from the mean.
Impact on the Mean
Outliers can dramatically distort the mean. If one person with a $1 billion income joins a group of ten, the average income becomes wildly unrepresentative. This is why income statistics typically prefer the median as a measure of central tendency.
However, carelessly removing outliers risks discarding important information. Distinguishing measurement errors from genuinely extreme values requires careful judgment.
Robustness of Percentiles
Percentiles are rank-based statistics and therefore nearly immune to outlier effects. No matter how much the top 0.1% increases their wealth, the 50th percentile (median) remains unchanged. This robustness is a major advantage of using percentiles for rankings.
Handling in MyRank
MyRank uses percentile-based ranking to minimize the influence of outliers in each indicator's data. Even when a user inputs a value outside the data range, it is appropriately handled as the highest or lowest rank. This ensures stable ranking results unaffected by extreme values.