Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 3m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample1h 1m
- 10. Hypothesis Testing for Two Samples2h 8m
- 11. Correlation48m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
1. Intro to Stats and Collecting Data
Intro to Stats
Problem 1.3.15
Textbook Question
In Exercises 9–20, identify which of these types of sampling is used: random, systematic, convenience, stratified, or cluster.
Criminology Researchers randomly selected 50 convicted felons from each category of burglary, auto theft, and assault.

1
Identify the different categories mentioned in the problem: burglary, auto theft, and assault.
Understand that the researchers are selecting a specific number of individuals (50) from each category.
Recognize that the selection of individuals from each category is done randomly.
Note that the categories represent different strata or groups within the population of convicted felons.
Conclude that this method of sampling, where random samples are taken from each stratum, is known as stratified sampling.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Stratified Sampling
Stratified sampling is a method where the population is divided into distinct subgroups, or strata, that share similar characteristics. Researchers then randomly select samples from each stratum to ensure representation across these groups. This technique is particularly useful when researchers want to ensure that specific segments of the population are adequately represented in the sample.
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Random Sampling
Random sampling is a technique where each member of the population has an equal chance of being selected. This method helps to eliminate bias and ensures that the sample is representative of the entire population. In the context of the question, the researchers randomly selected individuals from each category, which is a key feature of random sampling.
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Sampling Distribution of Sample Proportion
Population and Sample
In statistics, the population refers to the entire group of individuals or instances that researchers are interested in studying, while a sample is a subset of that population selected for analysis. Understanding the distinction between these two concepts is crucial, as the sample should accurately reflect the characteristics of the population to draw valid conclusions.
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