Which of the following best describes the difference between data and data?
Table of contents
- 1. Intro to Stats and Collecting Data1h 14m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically2h 5m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables3h 6m
- 6. Normal Distribution and Continuous Random Variables2h 11m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 23m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals15m
- Confidence Intervals for Population Mean1h 18m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - Excel28m
- Confidence Intervals for Population Means - Excel25m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 25m
- 9. Hypothesis Testing for One Sample3h 29m
- 10. Hypothesis Testing for Two Samples4h 50m
- Two Proportions1h 13m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 3m
- Two Means - Unknown Variances Hypothesis Test - Excel12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - Excel9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - Excel21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - Excel12m
- 11. Correlation1h 24m
- 12. Regression1h 50m
- 13. Chi-Square Tests & Goodness of Fit2h 21m
- 14. ANOVA1h 57m
4. Probability
Basic Concepts of Probability
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Join thousands of students who trust us to help them ace their exams!Watch the first videoMultiple Choice
Which of the following best describes the difference between a stratified sample and a cluster sample?
A
In a stratified sample, the population is divided into clusters and all clusters are sampled; in a cluster sample, only one stratum is chosen and sampled.
B
In a stratified sample, the population is divided into subgroups () based on a characteristic, and random samples are taken from each ; in a cluster sample, the population is divided into clusters, some clusters are randomly selected, and all members of chosen clusters are included.
C
Both stratified and cluster samples involve dividing the population into groups and randomly selecting individuals from each group.
D
A stratified sample involves randomly selecting entire clusters, while a cluster sample involves randomly selecting individuals from each stratum.
Verified step by step guidance1
Understand that both stratified sampling and cluster sampling involve dividing the population into groups, but the way these groups are used differs.
In stratified sampling, the population is divided into subgroups called strata based on a specific characteristic (e.g., age, income level). Then, a random sample is taken from each stratum to ensure representation across all subgroups.
In cluster sampling, the population is divided into clusters, which are often naturally occurring groups (e.g., schools, neighborhoods). Instead of sampling from every cluster, a random selection of entire clusters is made, and all members within those selected clusters are included in the sample.
Recognize that stratified sampling aims to improve precision by ensuring all strata are represented, while cluster sampling is often used for practical convenience and cost reduction by sampling entire groups.
Compare the definitions carefully to identify that the correct description is: stratified sampling involves sampling from every stratum, whereas cluster sampling involves selecting some clusters and including all members from those clusters.
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