Ch 45: Praxis Mathematics: Statistics

About This Chapter

This review chapter will have you ready for questions about statistics on the Praxis Mathematics content knowledge test. Our expert instructors, video lessons and practice quizzes are exactly what you need.

Praxis Mathematics: Statistics - Chapter Summary

Many States use the Praxis Mathematics Content Knowledge test for teacher certification or endorsement. If you're planning to take this test but are feeling a little rusty on the subject of statistics, look no further. We can help! Our chapter on statistics contains video lessons, both brief and thorough, created by our instructors who are experts in math and education.

Give your brain a break from boring lectures or the drudgery of textbook reading. Our videos are engaging, easy to navigate and very accessible. Watch them on any mobile device. You might even observe some teaching techniques you like enough to incorporate them into your teaching someday. Our instructors will lead you through reviewing topics including, but not limited to:

  • Descriptive vs inferential statistics
  • Simple random sampling
  • Measures of central tendency (mean, mode, median, etc) and distribution
  • Quantitative data
  • Analysis of data sets
  • Population and sample variance
  • Bar graphs, pie charts, frequency tables, histograms, dot & box plots, scatterplots
  • Interpreting slope, intercept, correlations, frequencies, regression and more

These animated lessons are short and they can be viewed in any sequence according to your existing knowledge. Don't skip over the lesson quizzes. They will give you a good idea of how well prepared you are for the test.

Objectives for the Praxis Math: Statistics Chapter

Several States use the Praxis Math test. Double check that your state is one of them and then let us help you review statistics in preparation. The computer-based multiple-choice test of 60 questions is divided into 2 content categories. With content that reflects this chapter's topics, the second category, Geometry, Probability, Statistics and Discrete Mathematics, accounts for 32% of the test.

Specifically, you could encounter questions that assess your ability to interpret data collected from measurements on single as well as multiple variables, categorically or quantitatively; use dot plots, histograms and box plots; calculate and interpret measures of central tendency and spread; fit deviation to a normal distribution to estimate population percentages; and create and interpret two-way frequency tables and scatterplots. You could also demonstrate your ability to solve problems using linear regression models in terms of rate of change; work with intercepts and correlations; understand the inferential nature of statistics regarding population parameters; and use simulation and inferences in data-gathering and interpretation. Our lessons will correlate and expound upon these test objectives.

11 Lessons in Chapter 45: Praxis Mathematics: Statistics
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.

What is Quantitative Data? - Definition & Examples

2. 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.

Simple Random Samples: Definition & Examples

3. 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!

What is Random Sampling? - Definition, Conditions & Measures

4. 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.

What is the Center in a Data Set? - Definition & Options

5. What is the Center in a Data Set? - Definition & Options

Finding the center in a data set can sometimes be a little confusing. This lesson will help you determine the correct method for finding the center in a data set, and when you are finished, test your knowledge with a short quiz!

Mean, Median & Mode: Measures of Central Tendency

6. Mean, Median & Mode: Measures of Central Tendency

By describing the data using central tendency, a researcher and reader can understand what the typical score looks like. In this lesson, we will explore in more detail these measures of central tendency and how they relate to samples and populations.

Normal Distribution: Definition, Properties, Characteristics & Example

7. 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.

Visual Representations of a Data Set: Shape, Symmetry & Skewness

8. Visual Representations of a Data Set: Shape, Symmetry & Skewness

Visual representations are a fantastic way of understanding and analyzing your data. Use this lesson to understand the characteristics of visual representations of data.

Spread in Data Sets: Definition & Example

9. Spread in Data Sets: Definition & Example

Identifying the spread in data sets is a very important part of statistics. You can do this several ways, but the most common methods are through range, interquartile range, and variance.

Maximums, Minimums & Outliers in a Data Set

10. Maximums, Minimums & Outliers in a Data Set

When analyzing data sets, the first thing to identify is the maximums, minimums, and outliers. This lesson will help you learn how to identify these important items.

Finding Percentiles in a Data Set: Formula & Examples

11. Finding Percentiles in a Data Set: Formula & Examples

Percentiles are often used in academics to compare student scores. Finding percentiles in a data set can be a useful way to organize and compare numbers in a data set.

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

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