Learn Without Walls

📄 Save or print this lesson:

Module 5 Pre-Assessment

Test your baseline knowledge of sampling distributions

About This Assessment

This pre-assessment helps us understand what you already know about sampling distributions before you start the module. Your responses are for learning purposes only—don't worry if you don't know the answers yet! That's what the lessons are for.

5 questions covering key sampling distribution concepts
No time limit – take your time
Not graded – this is just to measure your starting point
You'll take the same assessment after completing the module to measure your learning gains

1 What is a sampling distribution?

The distribution of individual values in a single sample
The distribution of a statistic (like x̄) from all possible samples of size n
The distribution of the entire population
The method used to select a random sample

2 The Central Limit Theorem states that:

All populations are normally distributed
Sample means will be normally distributed if n ≥ 30, regardless of population shape
Larger samples always have larger means
The sample mean equals the population mean

3 What is the difference between standard deviation (σ) and standard error (SE)?

They are the same thing
Standard deviation measures spread of individual values; standard error measures spread of sample means
Standard error is always larger than standard deviation
Standard deviation applies to samples; standard error applies to populations

4 If a population has mean μ = 100 and standard deviation σ = 20, what is the mean of the sampling distribution of x̄ for samples of size n = 64?

μₓ̄ = 100
μₓ̄ = 20
μₓ̄ = 64
μₓ̄ = 2.5

5 For a sampling distribution of proportions, the conditions for using normal approximation are:

n ≥ 30
np ≥ 10 and n(1-p) ≥ 10
p = 0.5
The population must be normally distributed

Remember: This is not graded. It's just to see what you know before starting the module.

Back to Module 5 Start Lesson 1