Exploratory Data Analysis (EDA) and descriptive statistics form only the initial steps of any data science project. The next important aspect in any data analysis process is testing for statistical significance. In simpler words, it is the verification step to check whether the results obtained during the EDA phase were really trustworthy or are they simply result of a chance. More often that not, pure chance (or randomness) plays such a huge role in the data collection process — due to budget, time, or ethical constraints — that it is essential to avoid getting fooled by a sample of data.

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