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Ch 4: Common Core HS Statistics & Probability: Making Inferences with Data

About This Chapter

Let us help you teach your high school students about making inferences with data to fulfill the requirements for Common Core. Lessons in this chapter offer advice about how you can utilize the lessons in your classroom in addition to learning how to recognize when students have fully mastered the standard.

Standard: Understand statistics as a process for making inferences about population parameters based on a random sample from that population. (CCSS.Math.Content.HSS.IC.A.1)

Standard: Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation. (CCSS.Math.Content.HSS.IC.A.2)

Standard: Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each. (CCSS.Math.Content.HSS.IC.B.3)

Standard: Use data from a sample survey to estimate a population mean or proportion; develop a margin of error through the use of simulation models for random sampling. (CCSS.Math.Content.HSS.IC.B.4)

Standard: Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant.. (CCSS.Math.Content.HSS.IC.B.5)

Standard: Evaluate reports based on data. (CCSS.Math.Content.HSS.IC.B.6)

About This Chapter

A solid understanding of how to use data to make inferences can help students plan experiments and determine which type of group to collect data from. Students who comprehend the elements required in making inferences can estimate parameters from sample data and develop a margin of error using simulation models.

The lesson topics for this standard include:

  • The difference between parameters and statistics
  • Measures used for random sampling
  • Gathering data for evidence of the strength of a model
  • Experiments versus observational studies
  • Differences between random selection and random allocations
  • Using simulation to determine significance of differences in groups
  • Using data to evaluate reports

You'll know your students grasp these concepts when they can define random sampling and design randomized experiments. These standards and lessons can prepare students for college and for careers in engineering, construction, finance or science.

How to Use These Lessons in Your Classroom

Below are suggestions from Study.com that you could include in your regular curriculum to help you meet Common Core standards.

Sample Identification Lessons

Assign the video lessons on samples and populations in research as a homework or in-class assignment. Choose a topic to be researched and have students identify what a random, stratified, quota or convenience sample would look like for this topic.

Random Selection and Random Allocation Lessons

Watch lessons on estimating parameters from sample data and developing a margin of error in class. Find a population survey that you can present in class. Have students find the mean and develop a margin of error using the statistical data.

Pre-Quiz and Post-Quiz Lessons

Have students take the quizzes for the chapters on using simulations to determine significant group differences and report evaluation to become familiar with these concepts. Watch the video lessons and then have students take the quizzes again to see how well they comprehended the information.

8 Lessons in Chapter 4: Common Core HS Statistics & Probability: Making Inferences with Data
Test your knowledge with a 30-question chapter practice test
Defining the Difference between Parameters & Statistics

1. 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.

Samples & Populations in Research: Definition

2. Samples & Populations in Research: Definition

When planning an experiment, you will likely use groups of participants. This lesson explores the types of groups an experimenter can collect data from and the reason why there are different groups.

What is Random Sampling? - Definition, Conditions & Measures

3. 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.

The Purpose of Statistical Models

4. 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

5. 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

6. 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.

Estimating a Parameter from Sample Data: Process & Examples

7. 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.

How Randomized Experiments Are Designed

8. 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.

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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