# Ch 1: Overview of Statistics: Help and Review

### 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 quantitative data or working with categorical data
- 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 statistics basics
- 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 Overview of Statistics 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 Overview of Statistics chapter exam to be prepared.**Get Extra Support**: Ask our subject-matter experts any basic statistics 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 an Overview of Statistics unit of a standard college statistics course. Topics covered include:

- Descriptive and inferential statistics
- Discrete and continuous data
- Nominal, ordinal, interval and ratio measurements
- Random selection and random allocation
- Confounding and bias in statistics

### 1. Descriptive & Inferential Statistics: Definition, Differences & Examples

Descriptive and inferential statistics each give different insights into the nature of the data gathered. One alone cannot give the whole picture. Together, they provide a powerful tool for both description and prediction.

### 2. Difference between Populations & Samples in Statistics

Before you start collecting any information, it is important to understand the differences between population and samples. This lesson will show you how!

### 3. Defining the Difference between Parameters & Statistics

Using data to describe information can be tricky. The first step is knowing the difference between populations and samples, and then parameters and statistics.

### 4. Estimating a Parameter from Sample Data: Process & Examples

One of the most useful things we can do with data is use it to describe a population. Learn how in this lesson as we discuss the concepts of parameters and samples.

### 5. What is Quantitative Data? - Definition & Examples

Watch this video lesson to find out the difference between saying you have seven apples and saying that those apples are delicious. You will learn about quantitative data and why it is useful.

### 6. What is Categorical Data? - Definition & Examples

Categorical data is one of two types of data that you can collect when conducting research. This lesson will teach you how to understand and use categorical data.

### 7. Discrete & Continuous Data: Definition & Examples

You might be surprised to find that data is more than just a collection of numbers. Data is divided into several categories, including discrete and continuous data. Find out why!

### 8. Nominal, Ordinal, Interval & Ratio Measurements: Definition & Examples

Different types of data can be grouped and measured in different ways. In this lesson, you will learn about nominal, ordinal, interval, and ratio measurements.

### 9. The Purpose of Statistical Models

Understanding statistics requires that you understand statistical models. This lesson will help you understand the purpose of statistics, statistical models, and types of variables.

### 10. Experiments vs Observational Studies: Definition, Differences & Examples

There are different ways to collect data for research. In this lesson, you will learn about collecting data through observational studies and experiments and the differences between each.

### 11. Random Selection & Random Allocation: Differences, Benefits & Examples

Random selection and random allocation are often confused with one another. This lesson will help you remember the differences between them and learn how to use each method.

### 12. Convenience Sampling in Statistics: Definition & Limitations

Convenience sampling is one of the most common types of sampling in research. This is because of the benefits that convenience sample brings to the researcher. However, there are some limitations. You will learn about both in this lesson.

### 13. How Randomized Experiments Are Designed

When reading research or when conducting your own, it is important to understand the basic concepts of randomized experimental design that are covered in this lesson.

### 14. Analyzing & Interpreting the Results of Randomized Experiments

Analyzing and interpreting the results of an experiment can be a confusing process, and it's easy to make mistakes. This lesson will help you understand the important factors of experiment analysis.

### 15. Confounding & Bias in Statistics: Definition & Examples

Statistics can be a powerful tool in research. Unfortunately, statistics can also have faults. In this lesson, you will learn about the faults in statistics and how to critically examine research.

### 16. Bias in Polls & Surveys: Definition, Common Sources & Examples

When Mark Twain commented that there were three types of lies, he included statistics in the count. In this lesson, we look at bias, one of the ways in which statistics can mislead, and in some cases, flat out lie to us.

### 17. Misleading Uses of Statistics

It can be too easy to present statistics in a way that is misleading. This lesson will cover the ways in which a statistic can be misleading and how to avoid and identify misleading statistics.

### 18. Causation in Statistics: Definition & Examples

In this lesson, you will learn about causation. In statistics, causation means that one thing will cause the other, which is why it is also referred to as cause and effect. When you are through, take a short quiz to test your understanding!

### 19. Deductive Argument: Definition & Examples

Intentionally or unintentionally, we reason deductively. In this lesson, we will examine the definition of deductive arguments, look at some uses and explore some examples of ways in which we reason or argue deductively.

### 20. Dot Plot in Statistics: Definition, Method & Examples

Dot plots are just one of the many methods used to organize statistical data. In this lesson, you will learn the definition of a dot plot, when it should be used, and how to create one.

### 21. Observational Study in Statistics: Definition & Examples

In this lesson, we will learn what an observational study is and look at some examples of such a study. We will also discuss some of the advantages and disadvantages of this type of study.

### 22. Skewness in Statistics: Definition, Formula & Example

In this lesson, you'll learn about skewness in statistics, including what data distribution and bell curves look like with and without skew. After that, you'll learn a formula to calculate skew, and then you can test your knowledge with a brief quiz.

### 23. Uniform Distribution in Statistics: Definition & Examples

In this lesson we will explore uniform distributions and learn how to identify two basic types: discrete and uniform. You'll also learn how to recognize both by the shape and characteristics of their graphs.

### 24. Confidence Interval: Definition, Formula & Example

When calculating the mean or proportion for a population, using samples and confidence intervals can make the calculation more manageable. Learn more about this process in this lesson.

### 25. Chi Square Distribution: Definition & Examples

Chi square distributions are a way of mapping the probabilities of values. In this lesson, we'll look at distributions represented in graphs and tables. We'll also look at an example that uses chi square distribution to test the independence of variables.

### 26. Chi Square Practice Problems

Chi square is a method used in statistics that measures how well observed data fit values that were expected. In this lesson we will practice calculating and analyzing the value of chi square.

### 27. Chi Square: Definition & Analysis

In this lesson, you will learn the definition of chi square and how it is used to determine whether a null hypothesis should be accepted or rejected. We will work through an example, then you can take a brief quiz to see what you learned.

### 28. How to Calculate a Chi Square: Formula & Example

In this lesson, you will learn the formula and method for calculating chi square. You can then take a brief quiz to see what you learned. You will learn how to interpret the value of chi square in a separate lesson.

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### Other Chapters

Other chapters within the Introduction to Statistics: Help and Review course

- 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
- Sampling: Help and Review
- Regression & Correlation: Help and Review
- Hypothesis Testing in Statistics