Lower quartile - (1.5*IQR)
Upper quartile + (1.5*IQR)
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They are called outliers
Data points that do not fit with the rest of a data set are known as outliers. These values are significantly different from the majority of the data, either much higher or lower, and can skew statistical analyses. Outliers may arise from variability in the data, measurement errors, or they could indicate a novel phenomenon worth investigating. Identifying and understanding outliers is crucial for accurate data interpretation.
Data that does not fit with the rest of a data set is known as an outlier. Outliers can skew statistical analyses and distort the interpretation of data. They can be caused by errors in data collection, measurement variability, or may represent true but rare occurrences in the data set. Identifying and handling outliers appropriately is crucial in ensuring the accuracy and reliability of data analysis results.
It i the smallest value in the data set and corresponds to the value of the left-most end of the whisker. Unless there were outliers, in which case it will be an "X" to the left of the left-whisker.
Outliers are observations that are unusually large or unusually small. There is no universally agreed definition but values smaller than Q1 - 1.5*IQR or larger than Q3 + 1.5IQR are normally considered outliers. Q1 and Q3 are the lower and upper quartiles and Q3-Q1 is the inter quartile range, IQR. Outliers distort the mean but cannot affect the median. If it distorts the median, then most of the data are rubbish and the data set should be examined thoroughly. Outliers will distort measures of dispersion, and higher moments, such as the variance, standard deviation, skewness, kurtosis etc but again, will not affect the IQR except in very extreme conditions.