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Understanding Sampling Distributions

Authored by Kristian Ollier

Other

University

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Understanding Sampling Distributions
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20 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the Central Limit Theorem and why is it important?

The Central Limit Theorem is irrelevant for small sample sizes.

The Central Limit Theorem states that all data distributions are uniform.

The Central Limit Theorem applies only to populations with a normal distribution.

The Central Limit Theorem is a fundamental statistical principle that ensures the normality of sample means.

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

List three reasons why a sample can be used to infer about a population.

Samples can be used to manipulate population data easily.

Larger samples guarantee better results regardless of selection.

Samples are always more accurate than populations.

1. Practicality and cost-effectiveness. 2. Quicker data collection and analysis. 3. Properly selected samples can represent the population.

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is standard error and how is it calculated?

Standard error is calculated as SE = n / s.

Standard error is calculated as SE = s / √n.

Standard error is the same as standard deviation.

Standard error is calculated as SE = s * n.

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Explain the concept of confidence intervals in statistics.

Confidence intervals are only applicable to qualitative data.

A confidence interval guarantees that the true population parameter is within the range.

A confidence interval is a single value that represents the true population parameter.

A confidence interval provides a range of values that likely contains the true population parameter, expressed with a specific confidence level.

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What are the different types of sampling methods?

Random sampling, stratified sampling, systematic sampling, cluster sampling, convenience sampling.

Qualitative sampling

Inferential sampling

Descriptive sampling

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How does bias affect the results of a sample?

Bias affects sample results by introducing systematic errors that misrepresent the population.

Bias has no effect on sample results.

Bias improves the accuracy of sample results.

Bias only affects qualitative data.

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the difference between random sampling and stratified sampling?

Random sampling selects individuals randomly from the entire population, while stratified sampling divides the population into subgroups and samples from each subgroup.

Random sampling requires prior knowledge of subgroups.

Stratified sampling is only used for small populations.

Random sampling guarantees equal representation from all subgroups.

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