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Approaches to Data Transformations and Their Impact on the Skewness Statistic For Seriously Skewed Distributions: Selected Cryptocurrencies' Data Explored

The normality of distribution presents the underlying assumption of many statistical methods. In some cases, the observed data do not always follow a symmetric bell-shaped distribution or approximately normal distribution. Therefore, data transformation is a commonly used approach to deal with skewed data distributions. This paper analyses the three most often used data transformations: square root transformation, log transformation, and inverse transformation, and their impact on seriously skewed distributions. The proposed data transformations applied to several cryptocurrencies resulted in different statistically significant outcomes, which might be useful for quantitative analysis of data and prognostic modelling.

Cryptocurrencies, data transformation, hypothesis testing, normal distribution, seriously skewed distribution, significance level

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