About This Chapter
How It Works:
- Find the lesson within this chapter that corresponds to what you're studying in the Data Analysis and Statistics chapter of your textbook.
- Watch fun videos that cover the data analysis and statistics concepts you need to learn or review.
- Complete the quiz after watching each video lesson to test your understanding.
- If you need additional help, rewatch the videos until you've mastered the material, or submit a question for one of our instructors.
You'll learn all of the algebra topics covered in the textbook chapter, including:
- The properties of the Bell Curve
- What terms like standard score, stanines, z-score, percentile rank and cumulative percentage mean
- Determining the difference between populations and samples
- How to develop and write a hypothesis
- What the types of probability sampling and non-probability sampling are
- How to avoid biased sampling
- Choosing data collection techniques and collecting data
- The advantages and disadvantages of different experimental designs
- How to draw conclusions from results
- Using sample proportion
Big Ideas Math is a registered trademark of Larson Texts, Inc., which is not affiliated with Study.com.
1. Normal Distribution: Definition, Properties, Characteristics & Example
In this lesson, we will look at the Normal Distribution, more commonly known as the Bell Curve. We'll look at some of its fascinating properties and learn why it is one of the most important distributions in the study of data.
2. Summarizing Assessment Results: Comparing Test Scores to a Larger Population
Assessment results can yield valuable information about the individual test-taker and the larger population of test-takers. This lesson will describe how to compare test scores to a larger population by explaining standard score, stanines, z-score, percentile rank and cumulative percentage.
3. 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!
4. What is a Hypothesis? - Definition & Explanation
A hypothesis is an educated prediction that can be tested. You will discover the purpose of a hypothesis then learn how one is developed and written. Examples are provided to aid your understanding, and there is a quiz to test your knowledge.
5. Probability Sampling Methods: Definition & Types
Choosing a sample is one of the most important steps in research. But how should you choose? In this lesson, we'll look at three types of probability sampling: simple random, systematic, and stratified sampling.
6. Non-Probability Sampling Methods: Definition & Types
There are many different ways to choose a sample for a research study. In this lesson, we'll look at three types of non-probability sampling: convenience, quota, and judgmental (or purposive sampling) and when to use each type.
7. What is a Biased Sample? - Definition & Examples
One goal of research is to obtain the best estimate for a population. The best estimate is an unbiased statistic representative of the population of interest. In this lesson, learn what biased samples are and how to avoid them in your research.
8. 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 potential biases in research.
9. Strategies for Choosing a Data Collection Technique
After figuring out what you are going to study, you, as the researcher, will need to figure out how to study it. This lesson discusses popular ways a researcher can collect data as well as why a researcher would chose a particular data collection technique.
10. 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.
11. Advantages & Disadvantages of Various Experimental Designs
There are many different options for researchers when deciding how to run a study. In this lesson, we'll look at some of the advantages and disadvantages of some common experimental designs.
12. Drawing Logical Conclusions from Experimental Data
Experimental results are what scientists like to share with each other, but it's important to understand what those data mean. We do this in the final step of the experimental process, when we draw meaningful conclusions from the results we obtained.
13. 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.
14. Sample Proportion in Statistics: Definition & Formula
This lesson talks about the definition, formula, and use of the sample proportion. We also see a brief intro into the concept of margin of error and selection of sample size. After completing the lesson, take a short quiz to see what you have learned.
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Other chapters within the Big Ideas Math Algebra 2: Online Textbook Help course
- Big Ideas Math Algebra 2 - Chapter 1: Linear Functions
- Big Ideas Math Algebra 2 - Chapter 2: Quadratic Functions
- Big Ideas Math Algebra 2 - Chapter 3: Quadratic Equations and Complex Numbers
- Big Ideas Math Algebra 2 - Chapter 4: Polynomial Functions
- Big Ideas Math Algebra 2 - Chapter 5: Rational Exponents and Radical Functions
- Big Ideas Math Algebra 2 - Chapter 6: Exponential and Logarithmic Functions
- Big Ideas Math Algebra 2 - Chapter 7: Rational Functions
- Big Ideas Math Algebra 2 - Chapter 8: Sequences and Series
- Big Ideas Math Algebra 2 - Chapter 9: Trigonometric Ratios and Functions
- Big Ideas Math Algebra 2 - Chapter 10: Probability