Extreemwaarden

Extreemwaarden, often translated as extreme values or outliers, are data points that lie significantly far from the main body of a dataset. They are values that are unusually high or unusually low compared to the rest of the observed data. The identification and handling of extreme values are crucial steps in data analysis because they can disproportionately influence statistical measures and the overall interpretation of results.

The presence of extreme values can skew statistical indicators. For instance, the mean (average) of a dataset is highly sensitive to outliers. A single very large or very small value can pull the mean significantly in its direction, making it a less representative measure of the central tendency of the data. Similarly, standard deviation, which measures the spread of data, can also be inflated by extreme values.

There are several methods to detect extreme values. Visual inspection of data through plots like box plots or scatter plots can often reveal outliers. Statistical methods include using Z-scores, where a data point is considered an outlier if its Z-score (number of standard deviations from the mean) exceeds a certain threshold (commonly 2 or 3). Another common approach is the interquartile range (IQR) method, where values falling below Q1 - 1.5*IQR or above Q3 + 1.5*IQR are flagged as potential outliers.

The decision on how to handle extreme values depends on their cause and the context of the analysis. They might be genuine but rare occurrences, errors in data collection or entry, or representative of a different underlying process. Depending on the situation, outliers might be removed, transformed, or analyzed separately to understand their specific impact. Ignoring extreme values can lead to misleading conclusions, while their arbitrary removal can also distort the data.