Outliers are one or more values in a data set that stand out from the others. For example, the five best horses are determined by their average lap time. An unusual lap time, either too good or too poor, is considered an outlier. But, how can one identify outliers from a large data set? One way is to find the interquartile range. Values above or below 1.5 times the IQR are considered outliers. The second method uses z scores. The values within minus two and plus two z scores are generally considered usual values, covering approximately 95% of data values. Anything outside this range is an outlier. The third method is using boxplots. Any data point that lies outside the whiskers of a box plot is considered an outlier. Outliers can affect the mean, standard deviation, and range of data, but some outliers can be ignored without affecting the sample statistic. So, careful considerations are made to consider outliers in calculations or trim them away.