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+---- Thread: How do we handle outliers in dataset (/showthread.php?tid=427)
How do we handle outliers in dataset - manjunath - 12-23-2022
Outliers are data points that lie outside the normal range of values in a dataset. They can have a significant impact on statistical analyses and can often distort the overall pattern of the data, so it is important to identify and handle them appropriately.
There are several different ways to handle outliers in a dataset, and the best approach will depend on the specific characteristics of the data and the goals of the analysis. Some common options include:
- Ignoring the outlier: If the outlier is an isolated point and does not fit the pattern of the rest of the data, it may be best to simply ignore it and focus on the rest of the data.
- Transforming the data: Sometimes, outliers can be caused by the scale of the data. For example, if one variable is measured in dollars and another is measured in cents, the scale of the data will be very different and this could lead to outliers. In these cases, it may be helpful to transform the data to a common scale (such as converting all variables to percentages) in order to make it easier to compare the values.
- Clipping the data: Another option is to "clip" the data by setting a maximum or minimum value beyond which data points will be excluded from the analysis. This can be useful if the outlier is a single extreme value that is distorting the overall pattern of the data.
- Imputing the data: If the outlier is a missing value, it may be possible to impute (or estimate) a value for it based on the values of other similar data points. This can be done using a variety of techniques, such as linear interpolation or k-nearest neighbors.
- Identifying and treating the cause: In some cases, outliers may be caused by a specific problem or error in the data collection process. In these cases, it may be helpful to identify and correct the cause of the outlier in order to eliminate it