![]() But if our data is fairly symmetrical or there aren’t outliers, then consider using mean and standard deviation for central tendency and spread, respectively. So if our data is skewed or if there are outliers, use median for central tendency and IQR for spread. But median and IQR can ignore these outliers, giving us more accurate measurements of the data. That’s because mean and standard deviation will take into account all points in the data set, including the outliers. ![]() When we have a data set with outliers that skew the data, the median will be a better measure of central tendency than the mean, and the interquartile range will be a better measure of spread than standard deviation. The rule says that a low outlier is anything less than ?Q1? (the first quartile) minus 1.5(IQR), and that a high outlier is anything greater than ?Q3? (the third quartile) plus 1.5(IQR).įor example, if ?Q_1=25?, ?Q_3=35?, and therefore ?\text=10?, then the low outliers would be the data points below ?25-1.5(10)=10? and the high outliers would be the data points above ?35 1.5(10)=50?. We use what’s called the 1.5-IQR rule, and it will identify both high outliers (outliers above the majority of the data) and low outliers (outliers below the majority of the data). But there’s also a technical way to calculate outliers. If there’s a data point that’s really far from most of the data, then we can probably call it an outlier. Oftentimes we can’t just “eyeball” an outlier. Outliers are data points that are unlike most of the rest of the data. Specifically, the majority of the data is clustered in one area, and there are one or more outliers away from the majority of the data. The reason we get skewed distributions is because data is disproportionally distributed.
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