Python Data Analysis Bootcamp class 9 - 06 Central Limit Theorem at work

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Robustness in statistics refers to the Central Limit Theorem's remarkable property, where the means of sufficiently large samples tend to approximate a normal distribution, regardless of the original distribution of the population, whether it's uniform, exponential, skewed, or abnormal. This fundamental insight underpins the reliability and applicability of statistical techniques, enabling us to make meaningful inferences, conduct hypothesis tests, and construct confidence intervals even in cases where the population distribution is unknown or departs from normality. It serves as a cornerstone in statistical analysis, enhancing our ability to draw reliable conclusions from data.

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