If sample size is n = 30 or greater, the distribution of sample means will also be normally shaped, regardless of the shape of ! the original distribution of scores. O True O False
If sample size is n = 30 or greater, the distribution of sample means will also be normally shaped, regardless of the shape of ! the original distribution of scores. O True O False
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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![**Understanding the Central Limit Theorem**
*Concept Check:*
If the sample size is \( n = 30 \) or greater, the distribution of sample means will also be normally shaped, regardless of the shape of the original distribution of scores.
- [ ] True
- [ ] False
*Explanation:* This statement relates to the Central Limit Theorem, which suggests that the distribution of the sample means approaches a normal distribution as the sample size becomes larger, typically \( n \geq 30 \), regardless of the original population distribution's shape. This concept is foundational in statistics, particularly for conducting hypothesis testing and creating confidence intervals.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fa37e4b63-b753-4d1a-8d4b-3db35940214f%2Fcb44e9e4-8f43-4521-b99b-c6730f97dd66%2F9xhzpv2_processed.jpeg&w=3840&q=75)
Transcribed Image Text:**Understanding the Central Limit Theorem**
*Concept Check:*
If the sample size is \( n = 30 \) or greater, the distribution of sample means will also be normally shaped, regardless of the shape of the original distribution of scores.
- [ ] True
- [ ] False
*Explanation:* This statement relates to the Central Limit Theorem, which suggests that the distribution of the sample means approaches a normal distribution as the sample size becomes larger, typically \( n \geq 30 \), regardless of the original population distribution's shape. This concept is foundational in statistics, particularly for conducting hypothesis testing and creating confidence intervals.
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