Understanding the Central Limit Theorem

What does the Central Limit Theorem help us understand?

• The process of flipping a coin

• The behavior of sample means with respect to the population mean

• The calculation of variances in a dataset

• The way to perform linear regression

What type of distribution was used to demonstrate the Central Limit Theorem?

• Binomial distribution

• Continuous uniform distribution

• Discrete unconventional distribution

• Normal distribution

What is impossible to get from the demonstrated distribution?

• A 3

• A 2

• A 1

• A 6

Tags

CCSS.6.SP.B.4

CCSS.HSS.ID.A.1

What was the sample size used in the first example of sampling?

• 20

• 2

• 4

• 10

Tags

CCSS.7.SP.A.1

CCSS.HSS.IC.A.1

What was the mean of the first sample of size 4?

• 3.25

• 2.75

• 2.5

• 3

Tags

CCSS.6.SP.B.5C

How many times did the presenter plan to take samples for plotting?

• 100

• 1000

• 5000

• 10000

Tags

CCSS.7.SP.A.2

What does an increased sample size do to the distribution of sample means?

• Makes it less normal

• Makes it approximate a normal distribution more closely

• Does not change its shape

• Turns it into a binomial distribution

What remains the same across different sample sizes when plotting sample means?

• The variance

• The distribution type

• The mean

• The sample size

What other aspect, besides the mean, could the Central Limit Theorem apply to?

• The range

• The sample sum

• The mode

• The median

Why is the normal distribution significant in statistics according to the Central Limit Theorem?

• It simplifies the calculation of probabilities

• It is rarely found in real-life data

• It represents the distribution of sample means well

• It is easy to calculate