DataHour: An Introduction to Central Limit Theorem
DataHour: An Introduction to Central Limit Theorem
27 Dec 202215:12pm - 27 Dec 202216:12pm
DataHour: An Introduction to Central Limit Theorem
About the Event
The Central Limit Theorem is a fundamental statistical principle that states that, under certain conditions, the distribution of the sum or average of a large number of independent and identically distributed random variables will be approximately normal (bell-shaped). This is true even if the individual variables themselves are not normally distributed.
The Central Limit Theorem has several important implications for statistical analysis. For example, it can be used to justify the use of the normal distribution as a model for the sampling distribution of the sample mean. It also provides a basis for the use of statistical techniques that rely on the assumption of normality, such as t-tests and ANOVA.
The Central Limit Theorem is often used in the analysis of data from experiments, surveys, and other observational studies. It can help to provide a statistical foundation for making inferences about a population based on a sample drawn from that population. The conditions under which the Central Limit Theorem holds true include:
- The random variables must be independent and identically distributed.
- The sample size must be sufficiently large (typically at least 30).
In this DataHour, Munmun will explain the logic, application and conclusions drawn from the Central Limit Theorem.
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Who is this DataHour for?
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- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
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