About This Chapter
MTEL Math: Samples & Populations - Chapter Summary
This chapter seeks to aid you in your studies for the MTEL Math exam through strengthening your understanding of samples and populations. Within these lessons, you will have opportunities to learn about the following:
- Samples and populations as used in research
- Conditions of random sampling
- Types of non-probability sampling methods
- Problems with non-probability and probability sampling
- Definition of a convenience sample
- Random assignment, random selection, and random allocation
- Randomized experiments and how to interpret and analyze them
- Populations, generalizability, and samples
- Differences between parameters and statistics
- Parameter estimations using statistics
These brief video lessons are accompanied by full transcripts that allow you to review the content at the pace you choose. If you would like to skip to a specific topic within the video lesson, you may utilize the Timeline feature. As you progress through the chapter, you will have the option to take self-assessment quizzes with each lesson and a cumulative exam upon completion of the chapter.
MTEL Math: Samples & Populations Chapter Objectives
One of the steps necessary to acquire your license to teach mathematics courses in Massachusetts is taking the MTEL Math exam. The 100 multiple-choice questions and 2 open-response questions of the exam are meant to assess your knowledge of math skills spread over a variety of content categories. Ten percent of the exam's questions focus on your understanding of data analysis, probability, and statistics. A firm grasp on samples and populations in statistics should help you understand the questions on this portion of the exam. Additionally, taking the multiple-choice quizzes in the chapter will expose you to the same question format that you'll find on the exam itself.
1. 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.
2. 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.
3. 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.
4. Issues in Probability & Non-Probability Sampling
Choosing a sample is an important part of research. The two methods of sampling both come with their own set of issues. In this lesson, we'll look at the issues with probability and non-probability sampling.
5. Convenience Sample: Example & Definition
Conducting true experiments is expensive and time-consuming. However, by using a sample of a population that is convenient, the cost and time required to conduct it are greatly reduced. So, why not do that for every experiment?
6. Random Assignment in Research: Definition and Importance
In order to get the most accurate results, researchers must choose and assign their subjects in a random manner. In this lesson, we'll look at random assignment, random selection, and why they are important.
7. 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.
8. 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.
9. The Relationship Between Population, Sample & Generalizability
Researchers try their best to gather a sample that represents their population. But why is this important? In this lesson, we'll look at the relationship between population, sample, and generalizability in research.
10. Population & Sample Variance: Definition, Formula & Examples
Population and sample variance can help you describe and analyze data beyond the mean of the data set. In this lesson, learn the differences between population and sample variance.
11. 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.
12. 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.
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Other chapters within the MTEL Mathematics (09): Practice & Study Guide course
- MTEL Math: Basic Arithmetic Operations
- MTEL Math: Absolute Value & Integers
- MTEL Math: Fractions
- MTEL Math: Decimals
- MTEL Math: Percents
- MTEL Math: Rates & Ratios
- MTEL Math: Proportions
- MTEL Math: Estimation
- MTEL Math: Origins of Math
- MTEL Math: Rational & Irrational Numbers
- MTEL Math: Complex Numbers
- MTEL Math: Properties of Numbers
- MTEL Math: Exponents & Exponential Expressions
- MTEL Math: Roots & Radical Expressions
- MTEL Math: Scientific Notation
- MTEL Math: Number Theory
- MTEL Math: Number Patterns & Sequences
- MTEL Math: Number Patterns & Series
- MTEL Math: Properties of Functions
- MTEL Math: Graphing Functions
- MTEL Math: Factoring
- MTEL Math: The Coordinate Graph & Symmetry
- MTEL Math: Linear Equations
- MTEL Math: Systems of Linear Equations
- MTEL Math: Vectors, Matrices & Determinants
- MTEL Math: Introduction to Quadratics
- MTEL Math: Working with Quadratic Functions
- MTEL Math: Polynomial Functions Basics
- MTEL Math: Higher-Degree Polynomial Functions
- MTEL Math: Piecewise, Absolute Value & Step Functions
- MTEL Math: Rational Expressions, Functions & Graphs
- MTEL Math: Exponential & Logarithmic Functions
- MTEL Math: Measurement
- MTEL Math: Perimeter & Area
- MTEL Math: Polyhedrons & Geometric Solids
- MTEL Math: Symmetry, Similarity & Congruence
- MTEL Math: Properties of Lines
- MTEL Math: Angles
- MTEL Math: Triangles
- MTEL Math: Triangle Theorems & Proofs
- MTEL Math: Similar Polygons
- MTEL Math: The Pythagorean Theorem
- MTEL Math: Quadrilaterals
- MTEL Math: Circles
- MTEL Math: Circular Arcs & Measurement
- MTEL Math: Analytic Geometry & Conic Sections
- MTEL Math: Polar Coordinates & Parameterization
- MTEL Math: Transformations
- MTEL Math: Data & Graphs
- MTEL Math: Statistics
- MTEL Math: Data Collection
- MTEL Math: Probability
- MTEL Math: Trigonometric Functions
- MTEL Math: Graphs of Trigonometric Functions
- MTEL Math: Trigonometric Identities
- MTEL Math: Applications of Trigonometry
- MTEL Math: Limits
- MTEL Math: Continuity
- MTEL Math: Rate of Change
- MTEL Math: Derivative Calculations & Rules
- MTEL Math: Graphing Derivatives & L'Hopital's Rule
- MTEL Math: Area Under the Curve & Integrals
- MTEL Math: Integration Techniques
- MTEL Math: Integration Applications
- MTEL Math: Differential Equations
- MTEL Math: Discrete & Finite Math
- MTEL Mathematics Flashcards