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Why do so many of life's events share the same characteristics as the central limit theorem? Why are estimations and confidence intervals important? When might systematic sampling be biased? Explain. What roles do confidence intervals and estimation play in the selection of sample size?

The response addresses the queries posted in 700 words with four references.

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The response addresses the queries posted in 700 words with references.

// As per the directions, we will write about the characteristics of central limit theorem that are shared in events of life. Then, we will write about the importance of estimations and confidence intervals.//

The Central Limit Theorem in concern to the non-mathematical definition can be stated that the aggregate or the mean of the large random variables or events is directed towards the formation of a bell-curve as it is there in normal distribution. For instance: If the case of rolling of a dice is taken, the distribution of the mean or the average of the numbers on the dice will be normally distributed. Each rolled number or events can be normally distributed. This is due to the reason that each event or rolled number is approximately equal or close to the mean or the average. It can be said that the lesser the event closer to the average or the mean, it has fewer chances of being normally distributed (Adams, 2009).

The large number of events in life occurs randomly. It is due to the multiple and differentiating factors with ...

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The response addresses the queries posted in 700 words with references.

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