Ch 1: Overview of Statistics: Tutoring Solution

About This Chapter

The Overview of Statistics chapter of this Statistics 101 Tutoring Solution is a flexible and affordable path to learning about statistics. These simple and fun video lessons are each about five minutes long and they teach all of the statistical principles and methods required in a typical statistics course.

How it works:

  • Begin your assignment or other statistics work.
  • Identify the statistical concepts that you're stuck on.
  • Find fun videos on the topics you need to understand.
  • Press play, watch and learn!
  • Complete the quizzes to test your understanding.
  • As needed, submit a question to one of our instructors for personalized support.

Who's it for?

This chapter of our statistics tutoring solution will benefit any student who is trying to learn statistics and earn better grades. This resource can help students including those who:

  • Struggle with understanding descriptive and inferential statistics, categorical and quantitative data, random selection or any other statistical topic
  • Have limited time for studying
  • Want a cost effective way to supplement their statistical learning
  • Prefer learning statistics visually
  • Find themselves failing or close to failing their statistics unit
  • Cope with ADD or ADHD
  • Want to get ahead in statistics
  • Don't have access to their statistics teacher outside of class

Why it works:

  • Engaging Tutors: We make learning statistics simple and fun.
  • Cost Efficient: For less than 20% of the cost of a private tutor, you'll have unlimited access 24/7.
  • Consistent High Quality: Unlike a live statistics tutor, these video lessons are thoroughly reviewed.
  • Convenient: Imagine a tutor as portable as your laptop, tablet or smartphone. Learn statistics on the go!
  • Learn at Your Pace: You can pause and rewatch lessons as often as you'd like, until you master the material.

Learning Objectives

  • Describe the differences between inferential and descriptive statistics.
  • Differentiate between populations and samples.
  • Explain the difference between parameters and statistics.
  • Estimate parameters from sample data.
  • Define quantitative, categorical, discrete and continuous data.
  • Become familiar with ordinal, interval, nominal and ratio measurements.
  • Use data to provide evidence for the strength of a model.
  • Compare and contrast experiments and observational studies.
  • Understand the difference between random selection and random allocation.
  • Discuss the limitations of convenience sampling.
  • Know how to design randomized experiments.
  • Analyze the data from randomized experiments and interpret the results.
  • Learn the implications of bias and confounding.
  • Identify the common sources of bias in polls and surveys.
  • Take a look at examples of how statistics can be used to mislead.

17 Lessons in Chapter 1: Overview of Statistics: Tutoring Solution
Test your knowledge with a 30-question chapter practice test
Descriptive & Inferential Statistics: Definition, Differences & Examples

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.

Difference between Populations & Samples in Statistics

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!

Defining the Difference between Parameters & Statistics

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.

Estimating a Parameter from Sample Data: Process & Examples

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.

What is Quantitative Data? - Definition & Examples

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.

What is Categorical Data? - Definition & Examples

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.

Discrete & Continuous Data: Definition & Examples

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!

Nominal, Ordinal, Interval & Ratio Measurements: Definition & Examples

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.

The Purpose of Statistical Models

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.

Experiments vs Observational Studies: Definition, Differences & Examples

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.

Random Selection & Random Allocation: Differences, Benefits & Examples

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.

Convenience Sampling in Statistics: Definition & Limitations

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.

How Randomized Experiments Are Designed

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.

Analyzing & Interpreting the Results of Randomized Experiments

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.

Confounding & Bias in Statistics: Definition & Examples

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.

Bias in Polls & Surveys: Definition, Common Sources & Examples

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.

Misleading Uses of Statistics

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.

Chapter Practice Exam
Test your knowledge of this chapter with a 30 question practice chapter exam.
Not Taken
Practice Final Exam
Test your knowledge of the entire course with a 50 question practice final exam.
Not Taken

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