What are some real-world implications of kurtosis in a dataset?
- Datasets with high kurtosis are easier to interpret
- High kurtosis can indicate a bias in data collection
- High kurtosis can indicate the presence of outliers
- Kurtosis does not have real-world implications
In real-world data analysis, kurtosis is used to identify the presence of outliers. High kurtosis in a dataset may signal an increase in tail risk. This is particularly relevant in fields like finance, where tail risk could translate into heavier losses than the normal distribution would predict.
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