When processing a large number of datasets which can potentially have different data distributions, we are confronted with the following considerations:

• Is the data distribution unimodal and if it is the case, which model best approximates it( uniform distribution, T-distribution, chi-square distribution, cauchy distribution, etc)?
• If the data distribution is multimodal, can we automatically identify the number of modes and provide more granular descriptive statistics?
• How can we estimate the probability density function of a new dataset?

This notebook tackles the following subjects:

• Histograms vs probability density function approximation
• Kernel density estimations
• Choice of optimal bandwidth: Silverman/ Scott/ Grid Search Cross Validation
• Statistical tests for unimodal distributions
• DIP test for unimodality
• Identification of the number of modes of a data distribution based on the kernel density estimation

Find the complete Medium post here