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Skewed data is characterized by a distribution with a long tail on one side. This can lead to differences between the mean, median, and mode, unlike in a normal distribution where these measures are typically aligned. Skewness can complicate statistical analysis because many parametric tests assume normally distributed data.
Data Transformation Techniques
log(x + 1)
to accommodate zero values.Non-parametric Statistical Tests
Visualization Techniques
Trimming or Winsorizing
Quantile Normalization
Robust Statistical Methods
Data Segmentation and Binning
Resampling Methods
Bayesian Methods
By applying these techniques, data scientists can effectively handle skewed datasets, ensuring more accurate and reliable statistical analysis. The choice of method depends on the specific characteristics of the data and the goals of the analysis.