Statistics for Data Science and Business Analysis - Understanding the central limit theorem

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5 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a sampling distribution of the mean?
A distribution of standard deviations
A distribution of population parameters
A distribution of individual data points
A distribution of sample means
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is it important to draw many samples when estimating the population mean?
To avoid any skewness in the data
To create a distribution of sample means that approximates the population mean
To increase the standard deviation of the sample
To ensure the sample mean is always equal to the population mean
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does the central limit theorem state about the distribution of sample means?
It will have a higher variance than the population
It will be identical to the population distribution
It will approximate a normal distribution
It will always be skewed to the right
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does the sample size affect the variance of the sampling distribution of the mean?
Larger sample sizes increase the variance
Larger sample sizes decrease the variance
Larger sample sizes make the variance equal to the population variance
Sample size has no effect on variance
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is the central limit theorem crucial for statistical analysis?
It allows for the use of normal distribution in analysis, regardless of the population distribution
It ensures all data is normally distributed
It guarantees that sample means are always accurate
It eliminates the need for large sample sizes
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