About This Chapter
NMTA Math: Sampling - Chapter Summary
The video lessons in this chapter examine sampling as a method of gathering information on a number of things or people. Watch the tutorial lessons to prepare to answer sampling questions on the NMTA examination. Recall what you've studied about stratified random and simple random sampling research techniques, and perhaps learn a few new concepts along the way. This chapter might help you to:
- Define and explain how to use simple random, cluster random, stratified random and systematic random sampling
- Demonstrate the central limit theorem
- Discuss the use of sampling distributions
- Calculate probabilities about means
You'll find that these lessons are convenient because they allow you to brush up on sampling facts that you may have forgotten as you prepare to take the NMTA Math examination. You can learn interactively at any location by using your Web-connected technology. Ask questions of the math experts and use the corresponding written transcripts as an extra study resource. When you're ready, take the self-assessment quizzes to test your retention of the chapter's material.
NMTA Math: Sampling Chapter Objectives
Sampling techniques, such as those you've studied in this chapter, are covered in the Statistics, Probability and Discrete Mathematics section of the NMTA Math examination. The equivalent of 19% of the entire test score, this section will determine your ability to solve problems with sampling and to teach related principles and techniques to your students. There are 150 multiple-choice questions on the computer-based examination.
1. 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.
2. 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!
3. Stratified Random Samples: Definition, Characteristics & Examples
Random sampling isn't always simple! There are many different types of sampling. In this lesson, you will learn how to use stratified random sampling and when it is most appropriate to use it.
4. Cluster Random Samples: Definition, Selection & Examples
Cluster random sampling is one of many ways you can collect data. Sometimes it can be confusing knowing which way is best. This lesson explains cluster random sampling, how to use it, and the differences between cluster and stratified sampling.
5. Systematic Random Samples: Definition, Formula & Advantages
Systematic random sampling is a great way to randomly collect data on a population without the hassle of putting names in a bag or using a random number generator. In this lesson, learn all about how and when to use systematic random sampling.
6. Sampling Distributions & the Central Limit Theorem: Definition, Formula & Examples
Want proof that all of this normal distribution talk actually makes sense? Then you've come to the right place. In this lesson, we look at sampling distributions and the idea of the central limit theorem, a basic component of statistics.
7. Find the Mean & Standard Error of the Sampling Distribution
Have you ever had a situation where one grade destroyed your average? Wouldn't you like a way of proving that your work was actually pretty good with that one exception? The standard error gives you such a chance.
8. Finding Probabilities About Means Using the Central Limit Theorem
The central limit theorem provides us with a very powerful approach for solving problems involving large amount of data. In this lesson, we'll explore how this is done as well as conditions that make this theorem valid.
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Other chapters within the NMTA Mathematics (304): Practice & Study Guide course
- NMTA Math: Properties of Real Numbers
- NMTA Math: Fractions
- NMTA Math: Decimals & Percents
- NMTA Math: Ratios & Proportions
- NMTA Math: Units of Measure & Conversions
- NMTA Math: Logic
- NMTA Math: Reasoning
- NMTA Math: Vector Operations
- NMTA Math: Matrix Operations & Determinants
- NMTA Math: Exponents & Exponential Expressions
- NMTA Math: Algebraic Expressions
- NMTA Math: Linear Equations
- NMTA Math: Inequalities
- NMTA Math: Absolute Value
- NMTA Math: Quadratic Equations
- NMTA Math: Polynomials
- NMTA Math: Rational Expressions
- NMTA Math: Radical Expressions
- NMTA Math: Systems of Equations
- NMTA Math: Complex Numbers
- NMTA Math: Functions
- NMTA Math: Piecewise Functions
- NMTA Math: Exponential & Logarithmic Functions
- NMTA Math: Continuity of a Function
- NMTA Math: Limits
- NMTA Math: Rate of Change
- NMTA Math: Derivative Rules
- NMTA Math: Graphing Derivatives
- NMTA Math: Applications of Derivatives
- NMTA Math: Area Under the Curve & Integrals
- NMTA Math: Integration Techniques
- NMTA Math: Applications of Integration
- NMTA Math: Foundations of Geometry
- NMTA Math: Geometric Figures
- NMTA Math: Properties of Triangles
- NMTA Math: Triangle Theorems & Proofs
- NMTA Math: Parallel Lines & Polygons
- NMTA Math: Quadrilaterals
- NMTA Math: Circles & Arc of a Circle
- NMTA Math: Conic Sections
- NMTA Math: Geometric Solids
- NMTA Math: Analytical Geometry
- NMTA Math: Trigonometric Functions
- NMTA Math: Trigonometric Graphs
- NMTA Math: Solving Trigonometric Equations
- NMTA Math: Trigonometric Identities
- NMTA Math: Sequences & Series
- NMTA Math: Graph Theory
- NMTA Math: Set Theory
- NMTA Math: Statistics Overview
- NMTA Math: Summarizing Data
- NMTA Math: Tables, Plots & Graphs
- NMTA Math: Probability
- NMTA Math: Discrete Probability Distributions
- NMTA Math: Continuous Probability Distributions
- NMTA Math: Regression & Correlation
- NMTA Mathematics Flashcards