About This Chapter
Who's it for?
Anyone who needs help learning or mastering college statistics material will benefit from taking this course. There is no faster or easier way to learn college statistics. Among those who would benefit are:
- Students who have fallen behind in understanding random sampling or working with the central limit theorem
- Students who struggle with learning disabilities or learning differences, including autism and ADHD
- Students who prefer multiple ways of learning math (visual or auditory)
- Students who have missed class time and need to catch up
- Students who need an efficient way to learn about sampling
- Students who struggle to understand their teachers
- Students who attend schools without extra math learning resources
How it works:
- Find videos in our course that cover what you need to learn or review.
- Press play and watch the video lesson.
- Refer to the video transcripts to reinforce your learning.
- Test your understanding of each lesson with short quizzes.
- Verify you're ready by completing the Sampling chapter exam.
Why it works:
- Study Efficiently: Skip what you know; review what you don't.
- Retain What You Learn: Engaging animations and real-life examples make topics easy to grasp.
- Be Ready on Test Day: Use the Sampling chapter exam to be prepared.
- Get Extra Support: Ask our subject-matter experts any sampling question. They're here to help!
- Study With Flexibility: Watch videos on any web-ready device.
Students will review:
This chapter helps students review the concepts in a Sampling unit of a standard college statistics course. Topics covered include:
- Stratified random samples
- Cluster random samples
- Systematic random samples
- Mean and standard error of the sampling distribution
- Law of large numbers
1. Simple Random Samples: Definition & Examples
Simple random sampling is a common method used to collect data in many different fields. From psychology to economics, simple random sampling can be the most feasible way to get information. Learn all about it in this lesson!
2. What is Random Sampling? - Definition, Conditions & Measures
Random sampling is used in many research scenarios. In this lesson, you will learn how to use random sampling and find out the benefits and risks of using random samples.
3. Stratified Random Samples: Definition, Characteristics & Examples
Random sampling isn't always simple! There are many different types of sampling. In this lesson, you will learn how to use stratified random sampling and when it is most appropriate to use it.
4. Cluster Random Samples: Definition, Selection & Examples
Cluster random sampling is one of many ways you can collect data. Sometimes it can be confusing knowing which way is best. This lesson explains cluster random sampling, how to use it, and the differences between cluster and stratified sampling.
5. Systematic Random Samples: Definition, Formula & Advantages
Systematic random sampling is a great way to randomly collect data on a population without the hassle of putting names in a bag or using a random number generator. In this lesson, learn all about how and when to use systematic random sampling.
6. Understanding the Law of Large Numbers
The law of large numbers is a concept that is often misunderstood in statistics. In this lesson, you will learn the real meaning of the law of large numbers and how it is employed.
7. Sampling Distributions & the Central Limit Theorem: Definition, Formula & Examples
Want proof that all of this normal distribution talk actually makes sense? Then you've come to the right place. In this lesson, we look at sampling distributions and the idea of the central limit theorem, a basic component of statistics.
8. Find the Mean & Standard Error of the Sampling Distribution
Have you ever had a situation where one grade destroyed your average? Wouldn't you like a way of proving that your work was actually pretty good with that one exception? The standard error gives you such a chance.
9. Finding Probabilities About Means Using the Central Limit Theorem
The central limit theorem provides us with a very powerful approach for solving problems involving large amount of data. In this lesson, we'll explore how this is done as well as conditions that make this theorem valid.
10. Systematic Sample: Definition & Example
There are many ways to take a sample of a population. In this lesson, we will discuss systematic sampling, what it is, and how to use it. We'll also consider the advantages and disadvantages of this method of sampling and then you can take a quiz!
11. Tally Chart: Definition & Examples
Explore earlier civilizations as to how and what methods were used to count. Also in this lesson we will learn how to create and interpret data from a tally chart.
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Other chapters within the Introduction to Statistics: Help and Review course
- Overview of Statistics: Help and Review
- Summarizing Data: Help and Review
- Tables and Plots: Help and Review
- Probability: Help and Review
- Discrete Probability Distributions: Help and Review
- Continuous Probability Distributions: Help and Review
- Regression & Correlation: Help and Review
- Hypothesis Testing in Statistics